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ECOLOGICAL FOOTPRINT, ECONOMIC GROWTH AND ECOLOGICAL EFFICIENCY
by
Hazrat Yousaf
PhD Scholar in Economics
Reg. # 01/PhD/PIDE/2011
A Dissertation Submitted in Partial Fulfilment of the
Requirement for the Degree of Doctor of Philosophy in Economics
Department of Economics Pakistan Institute of Development Economics
Islamabad, Pakistan 2011-2016
Certificate ofApproval,5
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's is to certify that the research work presented in this thesis, entitled: “Ecological Footprint,,
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i-conomic Growth and Ecological Efficiencv” was conducted by Mr. Hazrat Yousaf under the
'f’FuperV1s1on of Dr. Anwar Hussa1n and Dr. Samlna Khalll. No part of thls the31s has been
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11bmitted anywhere else for any other degree. This thesis is submitted in partial fulfillment ofe requirements for the degree of Doctor of Philosophy in Economics from Pakistan Institute1'Development Economics, Islamabad.
“%’-Student Name: 'Mr. Hazrat Yousaf Signature: mOl/PhD/PIDE/2011
:ipxamination Committee: h)a) External Examiner: Dr. Aneel Salman Signature:k' I WHOD/AssistantProfessor
Department ofManagement ScienceCOMSATS UniversityIslamabad
b) Internal Examiner: Dr. Rehana Siddiqui Signature:Head, Department of Environmental EconomicsPIDE, Islamabad
(\lder $19.?
if Supervisor: Dr. Anwar Hussain Signature: r \ 931.,Assistant Professor
f? PIDE, Islamabad
”Co-Supervisor: Dr. Samina Khalil Signature;gkfll"!
Director, Applied Economics Research Centre. University ofKarachi
Dr. Attiya Y. Javid Signature: fig:Professor/Head, Department ofEconomics \lPIDE, Islamabad
ii
Dedicated to
My Family
A Source of Inspiration throughout My Educational Career
iii
Acknowledgement
All glories to Allah Almighty, the Omniscient and the Omnipotent and His Benedictions may
be upon His Holy Prophet (Peace be upon him)-A saviour of mankind from darkness of
ignorance, a symbol to be and to do right. My deepest thank to Allah Almighty Who enable to
accomplish this task successfully with great devotions.
The acknowledgment would be inadequate, unless I express my deepest gratitude and
greatest appreciation to my worthy supervisors; Dr. Anwar Hussain, Assistant Professor, Pakistan
Institute of Development Economics (PIDE), Islamabad for his supervision and support. His wide
knowledge and comprehensible way of judgment have been of great importance for me. His broad
analysis and precise assessment enhanced not only the quality of this dissertation, but also complete
understanding of my thesis. I am thankful to him and Dr. Samina Khalil, Director Applied
Economic Research Centre (AERC), University of Karachi for their valuable guidance and
encouraging suggestions during the whole process.
I am immensely grateful to the Vice Chancellor of PIDE, Prof. Dr. Asad Zaman and our
faculty members; Dr. Musleh-ud-Din, Dr. Rehana Siddiqui, Dr. Ejaz Ghani, Dr. Attiya Yasmin
Javed (HoD of Economic Department), Dr. Eatzaz Ahmad, Dr. Karim Khan and Dr. Waseem
Shahid Malik and other teaching faculty who taught and guided me throughout my PhD program
at PIDE.
My special thanks go to Dr. Mathis Wackernagel, President Global Footprint Network USA,
for providing the dataset on ecological footprint. Dr. Wackernagel updated me with the recent
development in the area of ecological footprint. Besides this, the detailed comments and
constructive suggestions were given by two foreign referees; Professor Jeff Gow, Professor of
Economics in School of Commerce University of Southern Queensland Toowoomba QLD 4350,
Australia and Professor Simone D’Alessandro Associate Professor Department of Economics
and Management, University of Pisa, Italy and external examiner Dr. Aneel Salman, HoD
Department of Management Science, COMSATS University, Islamabad improved the quality of
the dissertation. I am indebted to their valuable conclusions and constructive comments. A
special credit goes to the two internal examiners in PIDE; Dr. Rehana Saddiqui and Dr.
Muhammad Nasir at the stages of proposal defence and before thesis submission. I am also
thankful to Dr. Syed Manzoor Ahmed, Dean Faculty of SSM&IT in Lasbela University of
Agriculture, Water & Marine Sciences (LUAWMS) and Mr. Tufail Hakeem secretary of
Pakistan Journal of Applied Economics, University of Karachi for their English proof reading.
I would like to express my deepest gratitude to all my family members and relatives. Special
thank is also due to my well-wishers, colleagues and friends, especially Dr. Kahild Khan, Head
of Economics Department in LUAWMS, Naveed Hayat, Fazal Hadi, Muhsin Ali, Ikramullah,
Ahsan Abbas, Muhammad Umar and administrative staffs (Muhammad Saleem and Saba-ul-
Haq) of Economics Department at PIDE for their precious cooperation.
Hazrat Yousaf
iv
Table of Contents
Table of Contents i
List of Tables vii
List of Figures x
List of Abbreviations xi
Abstract xii
Page#
Chapter One Introduction 1
1.1 The background 1
1.2 Significance of the Study 14
1.3 Objectives 15
1.4 Hypotheses 15
1.5 Organization of the Study 16
Chapter Two
Literature Review
17
2.1 Introduction 17
2.2 The concept of ecological footprint and its methodological issues of
estimation
17
2.3 Ecological footprint and economic growth 20
2.4 Ecological footprint and ecological efficiency 23
2.5 Environment and energy consumption 24
2.6 Ecological footprint and trade 28
2.7 Ecological footprint and working hours 30
2.8 Growth and energy consumption 32
2.9 Contribution of the study 34
Chapter Three The Theoretical Background 36
3.1 Introduction 36
3.2 The theoretical perspective of Neo-Malthusian: economic growth and
environment
37
v
3.3 The theoretical perspective of Neoclassical economists: economic
growth and environment
40
3.4 Ecological modernization perspective 42
3.5 World system and treadmill production perspectives 44
3.6 Export dependence perspective 45
Chapter Four Data and Methodology 46
4.1 Introduction 46
4.2 Data 46
4.3 Methodology 51
4.3.1 Atkinson Index of ecological footprint inequality based on environment
intensity and per capita income
55
4.3.2 Empirical specification of various influencing factors of ecological
footprint and its components
57
4.3.3 The expected theoretical linkages between dependent and
independent variables
62
4.4 Analytical tools 65
4.4.1 The computation of ecological efficiency 65
4.4.2 The computation of ecological efficiency index 65
4.4.3 The computation of environmental impact intensity 66
4.4.4 The computation of Atkinson index of equality 68
4.4.5 The Econometric modelling 68
4.4.5.1 The Fixed effect model 68
4.4.5.2 The Random effect model 72
4.4.5.3 The Hausman test 74
vi
Chapter Five Trends in the Ecological Footprint, Economic Growth
and Ecological Efficiency
75
5.1 Introduction 75
5.2 Trend of ecological footprints, resources consumption and socio-
economic variables
75
5.3 Trend of ecological footprints, economic growth and ecological efficiency 88
5.4 Analysis of ecological efficiency index, maximum and mean level of
ecological efficiency
90
Chapter Six Ecological Footprint, Environmental Impact Intensity and
Income Inequality
98
6.1 Introduction 98
6.2 Ecological footprint, environmental impact intensity and
income inequality of high income countries
98
6.3 Ecological footprint, environmental impact intensity and
income inequality of middle income countries
106
6.4 Discussion 114
Chapter Seven The Driving Forces of Total Ecological Footprint and
its Components
115
7.1 Introduction 115
7.2 The driving forces of total ecological footprint and its component in high-
middle income countries
115
7.3 The driving forces of total ecological footprint and its component of high
income countries
124
7.4 The driving forces of total ecological footprint and its component of
middle income countries
137
7.4 Discussion 148
vii
List of Tables Page#
Table 1.1: Averages per capita income, population and ecological footprint,
emissions and Biocapacity, 2003-2011
7
Table 1.2: Averages of export, import, agriculture, manufacturing,
services and urban population, 2003-2011
8
Table 1.3: Averages of export, import, agriculture, manufacturing,
services and urban population, 2003-2011
9
Table 1.4: Averages of services, built-up footprint and Biocapacity, 2003-2011
9
Table 5.1: Trend of Ecological Footprints and Its Components
(Global ha/person 2003-2011) of High Income Countries
76
Table 5.2: Total Ecological Footprints vs Biocapacity of High Income Countries
78
Table 5.3: Trend of Resources Consumption, 2003-11 of High Income Countries
79
Table 5.4: Trend of GDP, Population, Urbanization and Hours Works, 2003-11 of
High Income Countries
80
Trend 5.5: Trend of Export, Agriculture, Manufacturing and Services; 2003-11 of
High Income Countries
80
Table 5.6: Trend in Ecological Footprints and Its Components
(Global ha/person, 2003-2011) of Middle Income Countries
82
Table 5.7: Total Ecological Footprints vs Biocapacity of Middle Income Countries 84
Chapter Eight Conclusion and Recommendations
156
8.1 Introduction 156
8.2 Summary of the study 156
8.3 Policy recommendations 161
8.4 Limitations and direction for future research 163
APPENDIX A 164
APPENDIX B 169
APPENDIX C 178
APPENDIX D 181
APPENDIX E 192
REFERENCES 193
viii
Table 5.8: Trend of Resources Consumption, 2003-11 of Middle Income Countries
85
Table 5.9: Trend of GDP, Population, Urbanization and Hours Works; 2003-11 of
Middle Income Countries
86
Table 5.10: Trend of Export, Agriculture, Manufacturing and Services; 2003-11 of
Middle Income Countries
87
Table 5.11: Ecological Footprints, Economic Growth and
Ecological Efficiency; 2003-2011of High Income Countries
88
Table 5.12: Ecological Footprints, Economic Growth and
Ecological Efficiency; 2003-2011 of Middle Income Countries
89
Table 5.13: The gap between efficiency in resources utilization and
maximum level of ecological efficiency of High Income Countries; 2003-11
94
Table 5.14: The gap between efficiency in resources utilization and
maximum level of ecological efficiency of Middle Income Countries; 2003-11
96
Table 6.1: Atkinson Index of Equality: Total Footprint, Per Capita income,
And Environmental intensity, 2003-11 of High Income Countries
99
Table 6.2: Atkinson Index of Equality: Crop land Footprint, Per Capita income,
and Environmental intensity, 2003-11 of High Income Countries
100
Table 6.3: Atkinson Index of Equality: Grazing Footprint, Per Capita income,
and Environmental intensity: 2003-11 of High Income Countries
101
Table 6.4: Atkinson Index of Equality: Forest Footprint, Per Capita income,
and Environmental intensity, 2003-11 of High Income Countries
102
Table 6.5: Atkinson Index of Equality: CO2 Footprint, Per Capita income,
and Environmental intensity, 2003-11 of High Income Countries
103
Table 6.6: Atkinson Index of Equality: Fish Footprint, Per Capita income,
and Environmental intensity, 2003-11 of High Income Countries
104
Table 6.7: Atkinson Index of Equality: Built Up Footprint, Per Capita income,
and Environmental intensity, 2003-11 of High Income Countries
106
Table 6.8: Atkinson Index of Equality: Total Footprint, Per Capita income, 107
ix
and Environmental intensity, 2003-11 of Middle Income Countries
Table 6.9: Atkinson Index of Equality: Crop land Footprint, Per Capita income,
and Environmental intensity, 2003-11 of Middle Income Countries
108
Table 6.10: Atkinson Index of Equality: Grazing Footprint, Per Capita income,
and Environmental intensity: 2003-11 of Middle Income Countries
109
Table 6.11: Atkinson Index of Equality: Forest Footprint, Per Capita income,
and Environmental intensity, 2003-11 of Middle Income Countries
110
Table 6.12: Atkinson Index of Equality: CO2 Footprint, Per Capita income,
and Environmental intensity, 2003-11 of Middle Income Countries
111
Table 6.13: Atkinson Index of Equality: Fish Footprint, Per Capita income,
and Environmental intensity, 2003-11 of Middle Income Countries
112
Table 6.14: Atkinson Index of Equality: Built Up Footprint, Per Capita income,
and Environmental intensity, 2003-11 of Middle Income Countries
113
Table 7.1: The Driving Forces of Total Ecological Footprint:
High-Middle Income Countries (Random effect model)
117
Table 7.2: The Driving Forces of Components of Ecological Footprint:
High-Middle Income Countries (Random and Fixed effect models)
120
Table 7.3: The Driving Forces of Components of Ecological Footprint:
High-Middle Income Countries (Random and Fixed effect models)
123
Table 7.4: The Driving Forces of Total Ecological Footprint:
High Income Countries (Random effect model)
127
Table 7.5: The Driving Forces of The Components of Ecological Footprint:
High Income Countries (Random and Fixed effect models)
131
Table 7.6: The Driving Forces of The Components of Ecological Footprint:
High Income Countries (Random and Fixed effect models)
135
Table 7.7: The Driving Forces of Total Ecological Footprint:
Middle Income Countries (Random effect model)
139
Table 7.8: The Driving Forces of The Components of Ecological Footprint:
Middle Income Countries (Random and Fixed effect models)
143
Table 7.9: The Driving Forces of The Components of Ecological Footprint:
Middle Income Countries (Random effect model)
146
x
List of Figures
Page#
Fig. 1.1: Time trend of humanity’s ecological demand
10
Fig. 3.1: A circular flow of factors of production, environment and economy
36
Fig. 3.2: A graphical illustration of Ehrlich’s model
39
Fig. 3.3: Per capita consumption and its effect on the environment
40
Fig. 3.4: The environmental Kuznets curve 41
Fig. 3.5: The EMT channel of modernization regarding declining in
environmental Damage/ ecological sustainability
43
Fig.5.1: Percentage Share of Components of Ecological Footprints of
High Income Countries
76
Fig.5.2 : Percentage Share of Agriculture, Manufacturing & Services Intensity of
High Income Countries
81
Fig.5.3: Percentage Share of Components of Ecological Footprints of
Middle Income Countries
83
Fig. 5.4: Ecological Efficiency Index of High and Middle Income Countries: 2005-11
94
Fig. 5.5: Percentage share of components of total ecological footprint in
Middle Income Countries
95
xi
List of Abbreviations
EF Ecological Footprint
EE Ecological Efficiency
EEI Ecological Efficiency Index
EKC Environment Kuznets Curve
FAO Food Agriculture Organization
GDP Gross Domestic Product
GFN Global Footprint Network
RI Resource Intensity
STIRPAT Stochastic Impact by Regression on Population, Affluence and Technology
UN United Nations
UNDESA United Nations Department of Economic and Social Affairs
UNIDO United Nations Industrial Development Organization
NUDP United Nations Development Program
UNEP United Nations Environment Program
WDI World Development Indicator
WWF World Wildlife Fund
xii
ABSTRACT
The ecological footprint is one of the important environmental impact indicator of
humanity’s demand for crop, forest, fishing grounds, grazing and built-up land as well as for the
area of land required to assimilate CO2 emissions and waste generated by human activities. This
indicator describes resource budget and environmental degradation of globe, a region, a nation
or a city in a given year. This study examined trends of ecological footprint, economic growth
and ecological efficiency of middle and high income countries. It also estimated the gap between
a country’s efficiency in resource utilization and maximum ecological efficiency of total
footprints and its components. Besides, inequality in the distribution of income, environmental
impact intensity (or ecological efficiency) and ecological footprint for the group of middle and
high income countries is also estimated. The study used the panel dataset for the period 2003-
2011 that covered 35 High and 77 Middle income countries. The data on the Ecological footprint
was obtained from Global Footprint Network. The Stochastic Impact by Regression on
Population, Affluence and Technology (STIRPAT) model was used as an analytical tool to
examine the effect of various driving forces on total ecological footprint, cropland, forest, fishing
grounds, grazing land, CO2 footprint and built-up land footprint. The Atkinson Index was used
as an analytical tool to examine inequality between High and Middle income countries in
distribution of income, footprints and environmental impact intensity. The findings revealed that
the high income countries used more ecological resources than their biocapacity as compared to
middle income countries. The ecological footprint, GDP per capita, ecological efficiency, fossil
fuel consumption, and level of urbanization and service intensity of high income countries are
larger than middle income countries. While population density, annual working hours, and
manufacturing and services intensity of high income countries are lower than middle income
countries. Similarly, the sampled countries have more potential in cropland, forest and grazing
land activities, followed by CO2 footprint, fishing grounds and built-up land footprint for
achieving maximum level of ecological efficiency.
The regression analysis of combined panel supports the environmental Kuznets Hypothesis
in case of total ecological footprint and its components. The separate panel model regression
analysis of high income countries supports the hypothesis in case of total ecological footprint,
fishery, and grazing and built-up land footprint. The results of middle income countries of total
ecological footprint, cropland, CO2 footprint and grazing land footprint support the hypothesis
that decoupling of economic growth accelerates environmental sustainability. The major driving
forces that contribute to increase in total ecological footprint are economic growth, population,
xiii
level of urbanization, fossil fuel consumption, export intensity and income inequality. Similarly,
a rise in economic growth, population, export and manufacturing intensity, working hours, coal,
oil and gas consumption increases CO2 footprint of the sample countries. However, further level
of economic development and education improve environmental quality by reducing cropland,
fishing grounds and forest footprint. The comparison of resource distribution through Atkinson
Index shows that high income countries have larger equality in footprint and environmental
impact intensity than middle income countries in case of grazing land, forest, fishing grounds
and built-up land.
It is suggested that both high and middle income countries should control ecological
overshooting. Investment in education is instrumental in reducing the ecological footprint. Rural
areas should be developed through creating job opportunities, agro-based business activities and
small scale industries which will reduce pressure on built-up land footprint. Production and use
of renewable energy alternatives such as wind, solar system and micro hydro power plants can
lessen the CO2 footprint and also leads toward environmental sustainability. The high and middle
income countries should prioritize the utilization efficiency of cropland, forest and grazing land.
The high income countries should reduce their footprint associated with forest, CO2, fishing
grounds and built-up land, because its average environmental impact intensity is greater than
their biocapacity. The middle income countries should reduce cropland and grazing land
footprint due to their larger mean environmental impact intensity than high income countries.
ECOLOGICAL FOOTPRINT, ECONOMIC GROWTH AND ECOLOGICAL EFFICIENCY
by
Hazrat Yousaf
PhD Scholar in Economics
Reg. # 01/PhD/PIDE/2011
Supervisor
Dr.Anwar Hussain
Assistant Professor
Pakistan Institute of Development Economics, Islamabad
Co-Supervisor
Prof. Dr. Samina Khalil
Director, Applied Economics Research Centre
University of Karachi
A Dissertation Submitted in Partial Fulfilment of the
Requirement for the Degree of Doctor of Philosophy in Economics
Department of Economics Pakistan Institute of Development Economics
Islamabad, Pakistan 2011-2016
1
CHAPTER ONE
INTRODUCTION
1.1 The Background
In the last forty years, developed and developing/emerging economies have experienced
high economic growth, urbanization and per capita consumption of goods and services (UNDP,
2006; UNEP, 2007; Anders and John, 2009; GFN, 2014). The ecologists and environmentalists
have opine that these changes have increased environmental disaster (Goudie, 1981; Haberl,
2006; Nelson et al., 2006). In the past century, the population of the world has reached 7 billion,
whereas humanity’s resource consumption and residual emissions are faster than earth’s
regenerating capacity (Erb et al., 2007; Hoekstra, 2009; GFN, 2014). Extraction of natural
resources has reached to 45% in the last 25 years at global level (Turner, 2008; Krausmann et
al., 2009; Giljum et al., 2011; Behrens et al., 2007).
In case of emerging economies 559 million people live in cities of China, followed by India
with 329 million. Developed nations such as the United State of America has the largest urban
population which consist of 246 million (GFN, 2012). The highest per capita income and
transition from agriculture to industrialization generated more resource consumption, and
residual emissions (Foley et al., 2005; Haberl, 2006; Hertwich and Peters, 2009; Behrens et
al., 2007).
The increased carbon emissions i.e. more than 60% from the energy consumption to
facilitate the rapid economic growth has attracted an important concern for the environmental
sustainability between high and middle income countries (Adewuyi and Awodumi, 2017). The
increasing trend in CO2 emissions and energy consumption has accelerated climate change and
food security issues in different parts of the world. Similarly, modernity and market
liberalization have changed consumption of goods and services. However, different regions of
the world have different environmental impacts on the globe, due to differences in energy
2
consolidation, consumption of material goods and services, CO2 emissions, urbanization and
economic growth (Ahmed and Azam, 2016). Thus, the literature of development and
environmental economics examined the nexus between the material resource consumption,
economic growth, modernization and energy consumption. Since, the rapid economic
development and low CO2 emissions is the highest priority of the high income countries and
most part of their policies are concerned with the sustainable development. On the other hand,
middle-income countries are trying to achieve high economic growth by utilizing their material
resources and energy (GFN, 2016a; Zaman and Abd-el Moemen, 2017). The environmental
scientists argue that global warming, climate change and fossil fuel consumption are factors of
acceleration of CO2 emissions. Energy related CO2 emissions has increased by 19 percent and
will reach 25-90 percent in 2030 (Chen et al., 2016). The variation in the pattern of rainfall,
melting of snow and ice, raising the sea level, variation in the temperature of air and ocean,
worsening the wild life and agriculture productivity are mainly due to global warming and
climate change. Under these scenarios, the economists and environmentalists have turned their
attention from simple economic development into environmentally friendly economic
development in the last few decades. They argue that to decouple the economic growth, indeed
requires the environmental stability and environmental protection. The relationship between
economic development and environmental sustainability is complementary for sustainable
development (GFN, 2016a; Salahuddin et al., 2016).
The global climate change of the 21st century is one of the most important challenge facing
humans. Governments worldwide are trying to reduce the CO2 emissions because the
environmental sustainability has been worsened by CO2 emissions in the past two decades
(IPCC, 2014; Iwata and Okada, 2014). The humans’ activity in the form of energy consumption
for the years 2005 to 2013 reached to 60% (NBSC, 2016). The CO2 emissions of energy
consumption is 90% (IEA, 2015). According to Kyoto Protocol, developed countries and
developing countries are responsible for reducing CO2 emissions. The developed countries
3
provide financial assistance or environmentally friendly technology for developing countries
in this regard. The financial assistance is based on the Certified Emissions Reductions (CER)
by the developing countries (IEA, 2015).
Besides, human activity in the form of crop consumption, forest, grazing land, fisheries
and urbanization is more than regenerating capacity of the sphere since 1960s. The world’s
ecological footprint1 per capita in year 1961 was 2.27 gha2 per capita and reached to 3.01gha
in the year 2013. The biocapacity3 per capita in corresponding years was 3.12 gha and 1.73gha.
The ecological deficit in the year 2013 of Asia was 1.4 gha per capita. It requires 1.3 earths for
the regeneration of resource as consumed by Asia (GFN, 2016a). The CO2 emissions of this
region has increased significantly in the past two decades. In this region, the major CO2 emitter
is China where its share in the CO2 emissions of the world in 2013 is 28% (IEA, 2015). The
international community has asked China to reduce CO2 emissions and therefore China has
planned to reduce its CO2 emissions by 40-45 percent in year 2020 by reducing energy
consumption by 15 percent (GFN, 2016a; He et al., 2017).
The scenario of urban growth in high and middle income countries produces various issues,
for example greater demand for energy consumption and greater demand for material goods
and services. As urban population increased by more than 250 percent and it increased energy
consumption by 50 % (Al-mulali et al., 2013; Al-Mulali et al., 2015). The unsystematic and
unbalanced pattern of the urban development particularly in developing and emerging
countries, produce negative externality. More than 50 percent CO2 emissions are being emitted
by these regions (Behera and Dash, 2016). The effect of urbanization on economic
1 Ecological footprint shows impact of humans activities on environment, expressed in term of area of land
required to support humanity consumption in form of cropland, forestry, fishing grounds, grazing and built-up
land as well as the area of land to absorb the CO2 emission(GFN, 2014). 2 gha stands for global hectare and it is the unit measure of ecological footprint and biocapacity. One hectare is
approximately equal to 2.47 acres(Jorgenson and Burns, 2007; GFN, 2016a). 3 Biocapacity indicates the available productive area required to generate resources as well as to absorb
wastes(GFN, 2014).
4
development, energy consumption and CO2 emissions have been investigated by researchers.
However, its effect on the consumption material resource still requires the research work. In
this regard, the current study has utilized ecological footprint as material resources
consumption indicator to investigate the effects of driving forces. The increasing trend of
urbanization in middle income countries would lead to increase world’s urban population by
65 percent in 2050. Middle income countries are in the phase of industrialization and
urbanization, which led to more energy consumption. To meet the growing energy demand,
coal has become the first choice for rich resources and low cost benefits. However, coal
consumption is the primary source of CO2 emissions (He et al., 2017; Ouyang and Lin, 2017).
The major challenge to high-middle income countries is a sustainable development process.
The sustainable development is a rapid economic development process under durable
environment. In sustainable development; numerous works have investigated the impact of
economic development, energy consumption on the environment using the CO2 emissions as
environmental indicator. However, the CO2 emissions captured only a small part of
environmental damage due to the anthropogenic activity in the form of energy consumption,
cropland, fisheries, grazing land, and forestry and built-up lands (GFN, 2016a; Uddin et al.,
2017).
In the year 2014, global production of fish was 93.4 million tons, in which marine and
inland fisheries were 81.5 million tones and 11.9 million tons respectively. The major
contributors to this production are China, Indonesia, the United State and the Russian
Federation. The 87 percent of fish and fishery products are used directly for human
consumption and are used for non-food activities. The fish and fishery production provides a
significant share in the international trade of countries. As China is the main producer and
exporter of fish and fishery products. It is expected that the fish and fishery trade will increase
in the coming years due to climate change and food security. It will further deplete the fisheries
and will accelerate the footprint (FAO, 2016).
5
Due to increasing trend of population and more material resource consumption for luxury
life style and expansion in economic development have threatened the earth’s biocapacity. The
impact of human activities on environment and resource supply measured as biocapacity are
used as indicators for environmental sustainability (Khan & Hussain, 2017 ; Rashid et al.,
2018). The impact of human activities on environment is measured in term of ecological
footprint. It was developed primarily by (Wackernagel and Rees, 1996). It quantifies the
amount of area of land requires to support humanity’s demand for resource consumption and
assimilating residuals of a given population (Jorgenson and Burns, 2007; Knight et al., 2013).
The components of overall ecological footprint consist of cropland, forest, grazing land,
fisheries and built-up land footprint. The cropland, forest and fisheries footprints quantify the
production all crops, forest, fish and seafood products that a country uses. The grazing and
built-up area of footprints measure the area required for grazing of livestock, housing,
transportation, industry and hydroelectric power. It is an environmental impact indicator that
is related to ecological footprint and planet’s biocapacity (Monfreda et al., 2004). The unit
measure of footprint is global hectares (gha). The estimation and calculation of ecological
footprint is based on two main factors (Khan & Hussain, 2017 ; Rashid et al., 2018). Firstly, it
includes and keep record of crops, forest, fisheries, grazing and urban activities and energy
use. Secondly, these resources are converted into area of land for the impact of human activities
on environment.
In the following part of the study, we highlighted some descriptive statistics of developed
and developing countries to strengthen the significance of the study. Table 1.1 indicate that
Australia is the lowest populated country followed by the UK, where China and India are
relatively higher populated countries. However, per capita income of developed countries for
example Japan, UK, USA and Australia is much higher than developing countries and is
obtained through resource consumption and have deficit in their biocapacity. The results also
6
provide a clear message that in near future, particularly high and emerging economies would
increase imbalances in consumption of resources and disrupt the well-being of other regions
of the world. Thus, it was necessary to analyze the trend of urbanization, terms of trade,
husbandry, manufacturing and service activities of these nations.
7
Table 1.1
Averages per capita income, population and ecological
footprint, emissions and Biocapacity 2003-2011
Countries
Per
Capita
Income
in US$
Population
Million
EF CF BC
Biocapacity
Deficit or
Reserve
Number
of
Earths
required
China 3014 1327 2.49 1.5 0.93 -1.56 1.45
India 987 1204 0.91 0.4 0.5 -0.46 0.53
Pakistan 895 165 0.7 0.2 0.4 -0.3 0.40
Japan 37806 128 3.8 2.5 0.69 -3.11 2.21
UK 39848 62 4.15 2.3 1.37 -2.78 2.41
USA 45766 305 6.76 4.5 3.65 -3.11 3.93
Australia 40662 22 8.32 3.6 16.06 7.74 4.84
EF: Ecological Footprint, CF: Carbon Footprint BC: Biocapacity
Source: Global Footprint Network, www.footprint network.org and World Bank Data set
The major contributor to GDP in developed countries is the service sector while agriculture
and manufacturing sectors are the major contributor of GDP in developing nations. However,
the share of exports and imports as a percent of GDP of developed and developing nations.
The figure also shows that trade, population and services sector, the pressure on the resource
consumption of developed nations is more stressful than the developing nations. The trend
comparison of high-middle income countries in population, urbanization, income, coal, oil, gas
and other socioeconomic factors during 2003-11 are depicted in Appendix A.
8
Table 1.2
Averages of export, import, agriculture, manufacturing,
Services and urban population 2003-2011
Countries
Exports
% of
GD
Imports
% of
GDP
Population
in the
largest
city % of
urban
population
Agricultural
land % of
land area TOT
Manufacturing
% of GDP
Services
% of
GDP
China 28.21 24.34 3.04 54.86 6.65 39.64 43.83
India 21.30 25.10 5.71 60.51 13.80 16.05 52.38
Pakistan 13.80 19.33 22.50 46.59 7.55 14.89 53.89
Japan 15.08 14.98 32.12 12.70 5.77 19.26 71.51
UK 27.52 29.94 18.97 71.49 18.46 10.98 77.27
USA 11.82 15.97 7.451 34.78 6.25 12.94 77.71
Australia 19.86 21.13 22.70 54.69 8.61 9.43 69.82
TOT: Term of Trade
Source: World Bank Dataset
It has been projected that the population of the world will be 9.1 billion in 2050, as 34
percent higher than the current population (in 2015 of 6.5 billion). The urbanization will be 70
percent and income will be multiples than from year 2015. However, there will be more
resources required to meet the demand of the increasing population. In 2013, the biocapacity
of the world was 1.73 gha and the ecological footprint was 2.84 gha per capita, represents the
ecological deficit. Thus, there is an ecological deficit of 1.11 gha (FAO, 2016; GFN, 2016a).
The per capita crop footprint of USA and Australia is smaller than their biocapacity and
have biocapacity surplus of 0.4 and 2.2 gha per capita as shown in Table 1.3. The other nations
used their land for agricultural activities and have ecological overshooting. However, increase
in income, population and biocapacity deficit are the key influencing factors of the cropland
footprint in high-middle income countries.
9
Table 1.3
Averages of agriculture, crop footprint and Biocapacity: 2003-2011
Countries
Agricultural land
% of land area
Crop
footprint BC
Biocapacity Deficit
or Reserve
Number of Earths
required
China 54.86 0.51 0.30 -0.21 0.91
India 60.51 0.3 0.20 -0.10 0.53
Pakistan 46.59 0.29 0.28 -0.01 0.51
Japan 12.70 0.51 0.16 -0.35 0.91
UK 71.49 0.84 0.66 -0.18 1.50
USA 44.78 1.10 1.50 0.40 1.96
Australia 54.69 3.00 5.20 2.20 5.35
BC: Biocapacity
Source: Global Footprint Network, www.footprint network.org and World Bank Data set
The average built-up footprint in India, Pakistan and Japan is higher than their biocapacity.
It means that the demand of built-up land for urban population and services will increase in
these countries. It will further increase their environmental degradation as suggested by
biocapacity deficit. The trend comparison of ecological overshoot of high-middle income
countries during 2003-11 are depicted in Appendix B.
Table 1.4
Averages of services, built-up footprint and Biocapacity 2003-2011
Countries
Services %
of GD
Built-up
footprint BC
Biocapacity
Deficit or Reserve
Number of
Earths required
China 43.83 0.11 0.12 0.01 0.19
India 52.38 0.90 0.50 -0.40 1.60
Pakistan 53.89 0.70 0.40 -0.30 1.25
Japan 71.51 0.89 0.85 -0.04 1.58
UK 77.27 0.19 0.19 0 0.33
USA 77.71 0.10 0.20 0.10 0.17
Australia 69.82 0.07 0.08 0.01 0.12
BC: Biocapacity
Source: Global Footprint Network, www.footprint network.organd World Bank Data set
Besides, most of the developed and developing countries are in environmental deficit.
Humanity demand for material resources is more than the earth’s biocapacity. In year 2011,
global ecological footprint was 2.6 gha per capita, exceeding the available biocapcity of 1.7
gha per capita by 53%. The ecological footprint is one of the key indicators for environmental
10
sustainability, because it converts human activities into demand on earth’s regenerative
capacity (Kitzes et al., 2009; Galli et al., 2012). It describes the scenario of a nation’s footprint
by comparing it with biocapacity over time (Galli et al., 2012). The trend of ecological
footprint, its components and biocapacity of high-middle income countries depicted in
Appendix B, shows variation in demand for crop, fisheries, grazing land, forest, built-up land
and CO2 footprint because of differences in their consumption pattern and lifestyle activities
(Kitzes et al., 2009; GFN, 2014; Felix et al., 2016).
Similarly, during 2003-11, the burden of human activity during 2003-11 was greater than
the earth’s biocapacity, as depicted in Appendix B. However, the ecological footprint of high
income countries is far greater than the footprint of middle income countries. Figure 1.1 shows
human demand and comparing the same with earth’s ecological capacity for over the last 40
years. One vertical unit the figure corresponds to the entire regenerative capacity of the earth
in a given year. Human demand exceeds nature’s total supply from the 1970s onwards,
overshooting it by 53% in 2010.
Figure 1.1
Time trend of humanity’s ecological demand
11
The ecological footprint of high and middle income countries since 1960s has shown
inequality because the ecological footprint of high income countries has substantially increased
from 3.62 to 6.39 gha i.e. 76% average increased in its ecological footprint (Galli et al. (2012)).
The ecological footprint of middle income countries has increased from 1.84 gha in 1960 to
2.20 gha in 2005 i.e. 20% average increase in its ecological footprint which showed 40%
inequality between high and middle income countries in demand for ecological footprint (Galli
et al., 2012).
Likewise, they also presented inequality in CO2 and cropland footprint between high and
middle income countries. The CO2 footprint grew from 31% in 1965 to 63% in 2005 and
reduced cropland footprint from 37% to 18% in the same period of high income countries. The
CO2 footprint of middle income countries increased by 15% and cropland land reduced by 31
% during 1965-2005. The inequality between these nations in case of CO2 and cropland
footprint was 17% and 21% respectively, because of transformation from agricultural to
industrial societies and different impact of resources use inequality on its biocapacity supply
(Haberl, 2006; White, 2007; Galli et al., 2012). The inequality comparison of total ecological
footprint with cropland, forest, CO2 and built-up land footprint of 140 countries showed that
CO2 and forest footprint has larger inequality than total ecological footprint. The cropland and
built-up land footprint showed lower inequality than total ecological footprint in period 2003
(White, 2007). The Gini Coefficients of cropland and built-up land footprint were 0.27 and
0.39 respectively, lower than total footprint of 0.45 while the Gini Coefficients of CO2 and
cropland footprint were respectively 0.67 and 0.56, showing 65% and 34 % share for
inequality (White, 2007). The application of inequality through Gini Coefficient showed that
reduction in energy use by the nations that largely depend on energy would not just lead to
increase environmental sustainability but it also reduces inequality particularly the CO2 and
forest footprints. The application of inequality through Atkinson index linked total ecological
footprint with per capita income and environmental impact intensity (White, 2007).
12
The predefined condition for our society and economic development is a healthy planet.
Because, for the consumption of material goods and services indeed requires a clean and
sustainable environment. However, the increasing demand for resources has led to increase
pressure on environment. That is why it is more important to understand whether the humans
are under the Earth’s ecological capacity or not. As it requires 1.5 years for the production and
replenish of resources that are consumed in a single year by human. It implies that humanity
demand leads to generate ecological overshoot in resource consumption where ecological
footprint exceeded the earth’s biocapacity. The major contributor to ecological footprint is CO2
footprint. Its major sources are the fossil fuel consumption, urbanization, luxury life style and
industrialization (Khan & Hussain, 2017 ; Rashid et al., 2018). The CO2 footprint is three
times larger in year 2012 than it was in year 1961. It is argued that the ecological deficit
nations can operate their economic activities with reference to utilizing their own ecological
stock; importing resources from other nations and exploiting common environment by
releasing emissions from the fossil fuel consumption into the atmosphere (Xie et al., 2015).
The ecological overshoot is the result of depleted fisheries, deforestation, and biodiversity
loss and climate changes. As in the last four decades, more than 50 percent of vertebrate and
wildlife declined (Wang et al., 2012; GFN, 2016b; Sim and Park, 2016). It shows that the risk
to the earth’s ecosystem has increased.
These dynamics help us to link the resource consumption with its influencing factors,
because the environmental sustainability and sustainable development are become the targeted
goals by world-wide. This study has addressed issues related to resource usage, economic
growth and ecological efficiency in middle and high income countries. More specifically, this
study responded to questions: what is the trend in resource consumption in middle and high
income countries. Does high economic growth lead to depletion of resources? Does more
resource consumption lead to lower economic wellbeing: GDP? What components of
ecological footprint are used inefficiently? What are the effects of different factors on the use
13
of resources? What is the maximum level of resource consumption to achieve ecological
efficiency? What can be the policy options in light of our results for middle and high income
countries? Is inequality in per capita income, environmental impact intensity and total
ecological footprint and its components in high and middle income countries similar?
14
1.2 Significance of the Study
The significance of this study comes from the fact that the tension between economic
growth and environmental sustainability has become more debatable issue in the world. Over
the past two decades, CO2 emissions of fossil fuel, the consumption of crops, forestry, fish and
fishery products, grazing and built-up lands of high and middle income countries is more than
the earth’s biocapacity. It is due to the increasing trend in economic development, population,
urbanization and industrialization in countries of high-middle income. Therefore, this study
provides a significant contribution in the existence literature by focusing on three main
research questions. Firstly, it focuses on understanding the relationship among ecological
footprint, economic growth and ecological efficiency. Secondly, it tries to compare income
and environmental inequality between high-middle income countries4. Thirdly, it identifies the
driving forces that increase the ecological footprint. The findings will be more policy oriented
for formulating appropriate policies for environmental and sustainable development. This
study will provide a base line for further research in the area environmental economics.
4 This study performed analysis for high and middle income countries and excluded Lower income countries
due to unavailability of data on total ecological footprint, cropland footprint, forest, grazing land footprint,
fishing grounds, CO2 footprint and built-up land footprint for Lower income countries
15
1.3 Objectives
This study aims to meet the following objectives:
1. To examine trends of ecological footprint, economic growth and ecological efficiency
of middle and high income countries.
2. To estimate the ratio between a country’s efficiency in resource utilization and
maximum ecological efficiency of total footprints and its components.
3. To estimate inequality in distribution of income, environmental impact intensity or
ecological efficiency and ecological footprint for the group of middle and high income
countries.
4. To empirically test the impact of various driving forces on total ecological footprint,
cropland, forest, fishing grounds, grazing land, CO2 footprint and built-up land
footprint for High and Middle income countries.
1.4 Hypotheses
The following hypotheses are tested:
1. Fossil fuels coal, oil and natural gas, trade openness and share of manufacturing
goods lead to a higher carbon footprint.
2. Increase in urbanization, economic growth and population would lead to higher
ecological footprint.
3. The share of agriculture items in total export and domestic consumption of
agriculture goods would lead to vast utilization and thus higher footprint.
4. Consumption of animal products and livestock would have a tendency to
depletion of grazing land footprint.
5. Higher employment level, more working hours and services-manufacturing
intensity would lead to significant stress on the built-up footprint.
16
6. Higher expenditure on education would lower the depletion of crop and forest
land footprint.
7. Trend of footprints, economic growth and ecological efficiency in middle and
high income countries is similar.
1.5 Organization of the Study
This dissertation is divided into eight chapters. The background, significance, objectives
and hypotheses of the study have been given in chapter one. Chapter two covered empirical
literature, including the relationship between ecological footprints and economic growth;
ecological footprints and ecological efficiency; growth and ecological efficiency; ecological
footprints and its methodological issues for calculation. The theoretical foundations covering
theoretical perspective of neo-Malthusian economic growth and the environment; perspective
of neo-classical economists; ecological modernization; world system and treadmill production
and export dependence perspectives are discussed in chapter three. Chapter four focused on
the data and methodology, covering ecological efficiency index, environmental impact
intensity, Atkinson index of inequality, empirical specification of various influencing factors
of ecological footprints alongwith developing econometric models. Chapter five discussed
trend analysis of ecological footprints, economic growth and ecological efficiency alongwith
comparing total ecological footprint with its biocapacity and gap between maximum and mean
level of ecological efficiency. Chapter Six presented inequality in per capita income,
environmental impact intensity and ecological footprint through Atkinson index. Chapter
seven analyzed the impact of various driving forces of total ecological footprint, cropland
footprint, grazing land, forest, fishing grounds, CO2 footprint and built-up land footprint
between high and middle income countries. Chapter eight covered summary of the study, major
findings, policy implications and limitations/direction for future research.
17
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter covered empirical literature review classified into nine sections. The section
2.1 covered the introduction of the chapter. Section 2.2 focused on ecological footprint and its
methodological issues of estimation. Section 2.3 explained the relationship between ecological
footprints and economic growth, section 2.4 explained ecological footprints and ecological
efficiency, section 2.5 focused on the relationship between environment and energy
consumption, section 2.6 covered ecological footprint and trade, section 2.7 covered ecological
footprint and work hours and section 2.8 covered growth and energy consumption. Section 2.9
focused on the contribution of the study.
2.2 The concept of ecological footprints and its
methodological issues of estimation
The concept of ecological footprint is primarily developed by Wackernagel and Rees
(1996). It measures the area of land required to produce crop, forest, sea and river fishes,
grazing activities, built-up land for infrastructures and to assimilate CO2 emissions and waste
generated by a region, a nation or society in a given year. This is resource consumption
accounting tool by comparing ecological footprint and biocapacity. The biocapacity deficit is
occurred in case of total ecological footprint greater than biocapacity and reverse will be with
bicapacity larger than total ecological footprint. The unit of measurement of ecological
footprint and biocapacity is global hectares per capita (White, 2007; Daniel et al., 2008; Galli
et al., 2012; Uddin et al., 2017). The ecological footprint used for the analysis of inequality,
as an indicator for environmental sustainability, traced out the relationship between ecological
footprint and human development index for different nations of the world (Alessandro et al.,
2012; Galli et al., 2012; Uddin et al., 2017).
18
The estimation of ecological footprint which is based on the composition of cropland,
forest, grazing land, fisheries, built-up and CO2 footprint, however, has several methodological
issues based on the criticisms elaborated by the researchers for example Ayres (2000); Moffatt
(2000); White (2007); Daniel et al. (2008); Fiala (2008); Galli et al. (2012); Wim and Luc
(2014); Galli et al. (2016). They argued that the area of land required to support humanity
demand and to assimilate CO2 emissions and waste generated by human activities based on
various data sources like production statistics, World Food Organization, World Bank,
direction of trade statistics and other trade accounts of each nations and different energy
consumption statistics. This means the calculation of ecological footprint combines different
sources of data in one indicator. However, a single indicator may receive overestimation of
resources or double counting (Wim and Luc, 2014; Galli et al., 2016).
The second methodological issue arises in form of excluding the area of deserts and icecaps
land when calculating ecological footprint which leads to create biasness. There are many
examples where indigenous population live for livestock and mining activities in desert, arid
and semi-arid land. The third methodological issue is related to the fields forest, cropland,
grazing and fisheries areas where its footprint is hypothetical than actual. The accurate
estimation of its ecological footprint are hard. In particular, the various productive concepts of
land are not considered in case of ecological footprint estimates. For example, land used for
transport purposes, urbanization and mining is larger than the land used for grazing, forestry
and crop activities. Therefore, instead of accurate productivity, the earth’s average productivity
is not appropriate for estimating ecological footprint.
The fourth methodological issue is related to CO2 emissions. The ecological footprint
account only includes CO2 emissions generated from energy use. This does not include
emissions generated from other greenhouse gases and other sources like industrial process,
waste and nuclear power plant. However, Ivan and Anna (2005); Galli et al. (2012); Galli et
al. (2016) argued that the methodology of ecological footprint estimation proposed by
19
Wackernagel and Rees (1996) can be critiqued on the basis of 1) the difficulty in the surface
of area to the resources come from the sea; 2) assign average global productivities to the
components of ecological footprint instead of considering the land condition; 3) the constant
technology assumption for resources extraction; and 4) the biasness in case of CO2 emissions
only included from fossil fuel consumption.
Li et al. (2010) however, argued that the ecological footprint and biocapacity estimated by
the Global Footprint Network for globe, regions, nations and cities at a point in time as
annually. It underestimates the dynamic perspective of future resources. They further argued
that the ecological footprint account does not take into account the role of increasing future
consumption and change in technology. Because it constantly depends on the assumption of
constant technology. As for example it is stated that it would require 5 earths if everyone follow
the consumption pattern of America. It has not included that the importance of technological
change in technology and resource efficiency. Similarly, it found that the increasing
consumption pattern of developing countries could follow developed countries and before such
growth in consumption the technological change would be expected to occur. The account of
ecological footprint doesn’t answer such changes. Regarding built-up land footprint, it is
assumed that it occurs mostly in productive land. However, Valada (2010) argued that it is
expected particularly in case of Middle East and Asia where urbanization take place in arid
non-productive land. It, therefore creates biasness in ecological footprint account.
There are many other methodological issues in the ecological footprint estimation for
example yield productivity of products, arid and non-arid land, conversion of resources to
global hectare, the issue of equivalence factor of cropland, forest, grazing land and built-up
land. However, Galli et al. (2013); Galli et al. (2016) argued that the methodology issues in
ecological footprint estimation ignore the role of non-renewable fossil energy stock depletion
and monocultures in present increase of agriculture productivity.
20
Similarly, the choice of production and society’s lifestyles affects both the distribution and
use of the resources. The ecological footprint account ignored such possibilities on ecological
footprint demand, which promotes biasness in policy implementation (White, 2007; Daniel et
al., 2008; Galli et al., 2012). However, every measure has some merits and demerits. The
ecological footprint is not without methodological issues but it is still well suited for analysis
of various issues. For example how resources are distributed between different regions of the
world, comparison between biocapacity and ecological footprint by White (2007). The current
study estimates the trend of ecological footprint, economic growth and ecological efficiency
for high-middle income countries by using panel dataset for period 2003-2011 because
previous studies addressed hardly the trend analysis between these regions.
2.3 Ecological footprint and economic growth
In the field of environmental economics, researchers in the past few decades, especially
examining the relationship between economic development and ecological footprint. They
found positive, negative and even insignificant impact of economic development on footprint.
The studies like Dietz et al. (2003); Jorgenson and Burns (2007); Turner (2008); Jill et al.
(2009); Clark and Jorgenson (2011); Knight et al. (2013); Al-Mulali et al. (2015); Asici and
Acar (2016); Uddin et al. (2017) found the positive relationship between growth and footprint.
Jorgenson and Burns (2007) found that level of urbanization and service based activities
are major factors for a positive association between growth and footprint. These factors
increase demand for the crop, forest, animals and sea products as well as in industrial
production. It adds more consumption based environmental impact. However, urbanization is
the more responsible factor for environmental degradation. It increases energy usage and CO2
emissions (Cole, 2004; Cole and Neumayar, 2004; Jill et al., 2009; Jorgenson et al., 2010;
Kaneko and Poumanyvong, 2010; Al-mulali et al., 2013; Behera and Dash, 2016; He et al.,
2017). Jill et al. (2009) found that increasing use of energy in different sectors is responsible
21
reason for positive relations. Because, it produces CO2 emissions to environment in large
volume. Reduction of energy consumption causes to cut 50% CO2 footprint and yields non-
significant effect of growth on ecological footprints. Andrew and Brett (2011) added that the
military expenses and level of urbanization are responsible factors in case of positive nexus of
economic growth and ecological footprints. They over use the global environmental space,
demand for the production of armaments industries and built-up land.
Studies like Anders and John (2009); Knight et al. (2013) came to know that longer work
hours and employment to population ratio in business, agriculture, industries and mining leads
to generate a positive and significant association of economic growth with total ecological and
CO2 footprints. Some studies further added that the export factor is a factor which enhances
environmental degradation with economic growth. Because, export-related activities utilize
crop, forest, grazing, forest and therefore CO2 emissions occur (Jorgenson and Burns, 2007;
Jorgenson, 2009). However, the studies likeMostafa (2010); Yong et al. (2013); A. Usama et
al. (2014) found a negative association between footprint and growth. Mostafa (2010) on the
ground of inadequate distribution of income, argued that it increases environmental
degradation, generating negative association between growth and environmental impact
indicator i.e. total ecological footprint. Yong et al. (2013) found that due to trade openness,
capital inflow and different environmental policies contribute to bring variation in resource
usage and further economic growth, thus the negative association arises between growth and
ecological footprint. A. Usama et al. (2014) found negative effect of growth on footprint and
such result arises due to well-developed financial sectors and improvement in environmental
quality through environmental friendly technology. It reduces environmental degradation and
enhances economic growth. However, that does not mean that technology change only causes
negative association as Marco et al. (2008) pointed out that foreign direct investment through
dirty production process is responsible factor in this respect. Because clean environmental
22
technology reduces environmental degradation on one side, while foreign direct investment
increases both resource consumption and economic growth on the other hand.
The studies, likeKaneko and Poumanyvong (2010); Liddle and Lung (2010); Perry (2014)
pointed out that urbanization and urban density contributes to improve urban utilities which
lower energy usage and environmental degradation. The main focus of these studies based on
the cross sectional and panel dataset for total ecological footprint. They focused hardly on the
impact of various driving forces of the total ecological footprint and its components. As Uddin
et al. (2017) examined the impact of real income, financial development and trade openness
on the total ecological footprint, using the 27 highest emitting countries panel dataset for the
period of 1991-2012. They argued on the basis of empirical findings that the acceleration in
financial development and trade openness impact negatively on the total ecological footprint
while the growth in real income increases the footprint. They argued that real income growth
depends on the exploitation of domestic natural resource and eco-services. Therefore, an
increase in real income leads to increase the material resource and consequently increases the
ecological footprint. Asici and Acar (2016) argued that the environmental Kuznets curve
hypothesis validates for the relationship between income and ecological footprint of
production. However, when countries become rich, they export the ecological cost of their
consumption to poorer economies. Al-Mulali et al. (2015) argued that the validity of the
environmental Kuznets curve hypothesis depends on the status of the technologies in the
economic system. Technologies that promote energy efficiency, energy saving and renewable
energy support the hypothesis. Toth and Szigeti (2016) argued that the channel through which
the ecological footprint accelerated is over-consumption of material goods and services. It
accelerates the fossil fuel consumption and consequently emits the greater CO2 in atmosphere.
The growth in ecological footprint increased after industrial revolution. They further argued
that the environmental degradation is not population, but consumption pattern in developed
23
countries. The studies of Galli et al. (2016) are also supported the arguments. However,
Lanouar (2017); Mrabet and Alsamara (2017) on the empirical findings argued that the nexus
between ecological footprint and economic growth holds the U-shaped behavior. Since the
inclusion of trade openness, urbanization, electricity consumption and financial development
are worsening to ecological footprint and CO2 footprint. The results show that the improvement
in energy efficiency will decouple the nexus between ecological footprint and economic
growth. Therefore, the current study empirically estimates the association between total
ecological footprint, cropland, and forest, fishing grounds, CO2 and built-up land footprint with
economic growth for high-middle income countries by using panel dataset.
2.4 Ecological footprint and ecological efficiency
An ecological footprint is an environmental impact indicator of resource consumption,
where, ecological efficiency is the ability to produce economic output by using less resource
inputs and waste output. The various factors that influence the relationship between resource
consumption and ecological efficiency/environmental impact intensity. Dietz et al. (2003)
found that increase in affluence leads to increase ecological efficiency, but it doesn’t lead to
explain that environmental sustainability has been achieved. Because, a greater increase in
income leads to increase resource demand and consequently increase environmental damage
alongwith efficiency in resource utilization. Anders (2009) on the basis of longer work hours
increase economic output, the resource consumption and environmental impact intensity also
to grow. While, good management skills lower footprints and increase ecological efficiency
even in longer working hours. However, some studies York (2006, 2010) found that
improvement in technology increases ecological efficiency as well as consumption of
resources. York et al. (2009) found that increase in ecological efficiency is associated with an
increase in total ecological footprints and the availability of cheaper technology. This increases
the extraction of resources and environmental degradation. Due to limitation of panel dataset,
24
the main focus of these studies was on cross sectional dataset which led to generate biasness
because the economic structure varies from region to region and hence lead to different demand
for ecological footprint and ecological efficiency. Lu and Chen (2017) on the basis of the
empirical findings suggest that the ecological footprint can be more stable and ecological
efficiency can be improved in case of the steady consumption pattern and environmental
mitigation policy. Szigeti et al. (2017) examined the relationship among ecological footprint,
income and ecological efficiency for the year 2009. On the basis of empirical findings, it is
argued that many countries have increased the income and reduced the ecological footprint and
consequently improve the ecological efficiency. Ninety percent countries started to move in
the direction of sustainable development. Lanouar (2017)argued that the driving forces behind
the environmental improvement in the long-run are the economic development, urbanization
and life expectancy. They used the ecological footprint as a proxy for environmental
degradation. The current study, however, empirically estimated the trend of ecological
footprint, economic growth and ecological efficiency between high and middle income
countries through panel dataset analysis.
2.5 Environment and energy consumption
Researchers have conducted different studies in developed and developing countries
regarding the nexus of the economic growth and environment, through the EKC hypothesis.
The increasing trend of climate change, environmental degradation and resource use have
attracted the social scientists, for example Soytas et al. (2007); Ang (2008); Jalil and Mahmud
(2009); Jalil and Feridun (2011); Ilhan and Ali (2013); Gulden and Mehmet (2014); A. Usama
et al. (2014); Apergis and Ozturk (2015); Boluk and Mert (2015); Jebli and Youssef (2015);
Tutulmaz (2015); Li and Zhao (2016); Charfeddine and Mrabet (2017); Lanouar (2017) to
investigate the relationship between environmental quality and energy consumption. A. Usama
et al. (2014) estimate the impact of energy consumption on ecological footprint. They found
25
the positive association between energy consumption and ecological footprint. They argued
that due to the strong financial development, on which one side promotes more foreign direct
investment and on the other hand, trade openness and urbanization lead to increasing the
economic growth. The net effect of these activities increased demand for energy consumption
and the level of ecological footprint. This argument was also supported byJalil and Mahmud
(2009); Jalil and Feridun (2011); Ilhan and Ali (2013). They used CO2 as an environmental
impact indicator. However, Claessens and Feijen (2007); Tamazian et al. (2009) found the
negative association between environment and energy consumption. They argued that energy
efficiency and enterprise performance is increased by financial development and therefore
energy is negatively affecting CO2 emissions. Zhang and Cheng (2009) argued that the
technological changes increase the energy efficiency and reducing the CO2 emissions. Their
empirical estimates suggest that energy consumption is negatively related to CO2 emissions
for China’s economy. The literature of Pachauri and Jiang (2008); Dodman (2009); Liu (2009);
Lin and Liu (2010); Liu (2012); Zhu and Peng (2012) argued that due to increase in public
infrastructure efficiency such as public transportation, reduces energy consumption and CO2
emissions. However, the other side literature argued that because of low energy efficiency, low
level of energy saving policies and an absence of environmental awareness are generating
positive association between energy consumption and environmental degradation. Shi-Chun et
al. (2016) explored how urbanization generates a positive association between energy
consumption and CO2 emissions. It is found that due to huge pressure of public infrastructure
energy consumption is positively related to CO2 emissions and environmental degradation.
However, the study like Gulden and Mehmet (2014) tested the theory of disaggregation of
energy consumption into fossil and renewable energy consumption on environment used CO2
emissions as a proxy for environmental degradation. They argued that the relationship between
CO2 emissions and energy consumption takes the form of an invert-U shape. The findings did
26
not support the EKC hypothesis because of mix energy consumption technology rather than
renewable energy technology and lack of strong regulatory policy for the reduction of CO2
emissions. Most of them did not support the EKC hypothesis when using total ecological
footprint as an environmental impact indicator and focused on cross-sectional dataset. Lanouar
(2017) argued that growth in energy consumption increases the environmental degradation
particularly in the oil-exporting countries. Charfeddine and Mrabet (2017) argued that the
nexus between environment and economic development holds the environmental Kuznets
curve hypothesis due to the electricity consumption and financial development are negatively
affecting the environment. The author used the CO2 footprint as a proxy for environmental
degradation. Boluk and Mert (2015); Li and Zhao (2016) argued that due to the trade openness,
the impact of energy consumption on the CO2 emissions is positive. As economy is more open
in term of trade, acceleration in transportation and industrial activities increase the demand of
the fossil fuel. It consequently increases CO2 emissions. They further added that, besides, fossil
fuel, promote energy efficiency and near location of export industrial zones minimize the
environmental degradation. This result is also supported by the findings of Jebli et al. (2016)
in case of the OECD countries. They argued that to compete with the environmental
degradation, the efficient strategies are more trade and more use of renewable energy
consumption. However, Bilgili et al. (2016) investigated the impact of economic development
and energy consumption on the environment, using the CO2 emissions as a proxy for the
environmental degradation. They argued that the energy consumption from renewable sources
and improvement in energy efficiency are the responsible factors behind the negative impact
of energy consumption on the environment. The implication of renewable energy consumption
in different sectors of an economy decreases the impact of economic development on the CO2
emissions.
27
Boluk and Mert (2015) investigated the impact of renewable energy and economic growth
on the CO2 emissions. They argued that the implication of energy consumption from renewable
sources effects CO2 emissions negatively. However, further economic development based on
the use of renewable energy consumption increases CO2 emissions. Therefore, the net effect
in a country case is ambiguous. Kang et al. (2016) argued that the urbanization and the coal
combustion are the most important elements in which CO2 emissions have increased. The
urbanization increases demand for material goods and services while the coal consumption due
to its lower price and abundant volume increases the environmental degradation. Therefore,
the expansion of urbanization and trade has become the harmful determinants to the
environment in case of some high-middle income countries. In order to limit CO2 emissions,
energy needs renewable energy and efficiency. This is supported by the findings of Ahmed
and Azam (2016); Ahmad et al. (2017) that in order to achieve higher economic growth and
minimize the environmental degradation requires the replacement of environmentally friendly
technology. However, the environmental awareness and environmental regulations can reduce
the degradation of the environment and mitigate the climate changes (Ozokcu and Ozdemir,
2017).
Ali et al. (2016); Ali et al. (2017) claim that foreign direct investment via transformation
of dirty technology to middle income countries from high income countries increase the CO2
emissions instead of reduction in it. During the period of 1971-2012, they investigated the
environmental Kuznets curve hypothesis in Malaysia context. The reduction in environmental
degradation is not only from the reduction in output but the increase in environmental
regulations activities (Apergis et al., 2017). The current study empirically estimated the
relationship between energy consumption with total ecological footprint and CO2 footprint as
environmental impact indicators for high and middle income countries separately.
28
2.6 Ecological footprint and trade
With the current increasing trend of environmental degradation and the nexus between
trade and environment, much attention was given to the field of environmental economics
where like Jorgenson and Rice (2005); Rice (2007); Anders and John (2009); Jorgenson and
Clark (2011) conducted the impact of trade on the environment by using different indicators
as environmental degradation. While some previous studies, like Tobey (1990); Low and
Yeates (1992); Beghin and Poitier (1995); Jaffe et al. (1995); Strutt and Anderson (1999);
Dean (2002); Copeland and Taylor (2003); Hossain (2012); Ilhan and Ali (2013); Shahbaz et
al. (2013a); A. Usama et al. (2014); Summaiya et al. (2015); Farhani et al. (2016); Halicioglu
and Ketenci (2016); Rudolph and Figge (2017) investigated the environmental impact on trade
pattern in case of developed and developing countries and found different outcomes.
The studies, like Beghin and Poitier (1995); Strutt and Anderson (1999); Mohammad et al.
(2012); Rubaiya (2012); Summaiya et al. (2015) argued that trade liberalization leads to
enhance environmental degradation while the findings of Dean (2002); Cole and Elliott (2003);
Cole (2004); Derek (2008); Eunho et al. (2010) argued that environmental degradation is
negatively associated with trade. However, from the last few decades the environmental
sociologist conducted the ecological footprint-trade nexus after the introduction of ecological
footprint as consumption based environmental impact indicator. They tested the ecologically
unequal exchange hypothesis that due to favorable term of trade and disproportion flow of raw
material and natural resources consumption from less developed to more developed countries
leads to increase the environmental degradation in the form of greater use of the total ecological
footprint. To test the hypothesis, Jorgenson and Rice (2005); Rice (2007) used weighted index
of vertical flow of export from less developed to developed countries and found a negative
association between export and ecological footprint.
29
Similarly, Jorgenson and Rice (2005); Jorgenson (2009), empirically tested the export
dependency perspective, using the value of export as a percentage of GDP and found a negative
impact on the total ecological footprint. However, these findings support the ecologically
unequal exchange and export dependency perspectives that export is negatively related to the
ecological footprints. The studies, like Anders and John (2009); A. Usama et al. (2014) found
a positive association between ecological footprint and export because of inclusion of other
determinants like working hours per employee, military expenditure, service intensity, energy
consumption and urbanization of environmental degradation. However, Hossain (2012) argued
that the energy consumption and trade openness have unidirectional effect on CO2 emissions.
Ilhan and Ali (2013) examined the causal relationship between financial development, trade,
economic growth and carbon emissions. The findings suggested that as a share of trade in GDP
increases, it increases the demand of energy use and consequently increases the CO2 emissions.
Shahbaz et al. (2013a) argued that trade openness improves the environmental quality due to
the application of environmentally friendly technology. They further argued that the
combination of higher degree of financial system development and trade openness through
technique effect can increase environmental quality. Shahbaz et al. (2013a); Shahbaz et al.
(2013b); Boutabba (2014) argued that the financial development and economic growth
increase the trade openness and therefore increases the environmental degradation particularly
in case of developing countries. The developing countries are mainly depended on application
of dirty, polluting technology in their industrial sectors. Farhani et al. (2014) investigated the
dynamic relationship between the environment, economic growth, energy consumption and
trade openness. There is bi-directional relationship between the variable and the unidirectional
relationship exists between economic development and energy consumption to the CO2
emissions. Halicioglu and Ketenci (2016) investigated the nexus between environment and
trade openness.
30
The empirical findings support the environmental Kuznets curve and displacement
hypotheses. The higher environmental polluting countries are trying to reduce environmental
degradation by exporting the energy intensive goods while the transition countries for the
acceleration of economic growth are exporting the energy intensive products. Thus, the
environmental degradation is displaced from one region to transition region. Rudolph and
Figge (2017) examined the environmental consequences of globalization, by using the
ecological footprint as a consumption-based environmental impact indicator. They used the
economic globalization, social and political globalization indices of globalization. Economic
and political globalization has increased ecological footprint, they added. It is because of
increasing demand for energy and material goods and services. Social globalization improves
environmental sustainability due to the better coordination among different society groups.
The newly industrialized countries are trying to accelerate their trade openness and therefore
greater demand for energy consumption. All these lead to increase the environmental
degradation. All these studies limited to the nexus between trade and total ecological footprint
and hardly focused on the nexus between trade, total ecological footprint and its components.
The current study, however, empirically estimated the impact of trade on total ecological
footprint and CO2 footprint by using a panel data set for high and middle income countries
separately.
2.7 Ecological footprint and work hours
The scholars investigated the association of working hours with varieties of issues like
employment, income inequality, and time preference, consumption of goods and services and
well-being. The economists, particularly, have drawn many conclusions by taking into account
the influences of other factors, for example wages, length of time, location, urbanization,
education in case of association between environment and working hours (Knight et al., 2013).
Bowles and Park (2005); Oh et al. (2012) linked working hours with income inequality,
31
individual income and preferences where they argued that working hours is negatively
associated with income inequality while positively related to individual income and
expenditure that leads to enhance well-being. There are also studies ofReynolds (2004);
Galinsky et al. (2005); Otterbach (2010) who investigated the relationship between work time
preference and actual working time. Similarly, Alesina et al. (2005); Pouwels et al. (2008)
explored the association between working hours and well-being where they argued that longer
working hours lead to lower happiness in the case of Europe and America because of reduction
of free time for other activities. However, many Scholars are currently investigating the
association between working hours and environment, particularly by using ecological footprint
and CO2 emissions as environmental impact indicators (Knight et al., 2013). David and Mark
(2006) explored how the working hours are positively associated with energy consumption and
environment degradation, where they argued under controlling employment to population
ratio, labor productivity and population that, increase in working hours for production lead to
more demand for energy consumption and consequently increases environmental degradation.
The studies of Joliet (2005); Robinson (2006); Lajeunesse (2009); Coote et al. (2010)
argued that the reduction in working hours, meaning reduction of total paid up labor class leads
to reduce consumption of goods and services and then consequently reduce the environmental
degradation. Studies like Anders and John (2009); Knight et al. (2013) explored how working
hours are positively related to ecological footprint, CO2 footprint and CO2 emissions by
relaxing the restrictions of employment to population ratio, labor productivity, where they
argued that increase in working hours increase environmental degradation because of the
increasing demand for employment and labor productivity which further increases production
and demand for energy consumption and working hours. However, due to favorable term of
trade and export of inputs to more developed countries from less developed countries leads to
more environmental degradation even in case of lower working hours Anders and John (2009).
32
However, Anders and John (2009) further argued that the impact of working hours is
positively related to environmental degradation both developed and developing countries
because of larger extraction and consumption of total ecological and CO2 footprint. Juan and
Jordi (2013) compared the impact of two scenarios 1) by keeping current work hours constant
2) and reducing work hours on environment, where it found that second scenario significantly
lower the growth rate of income and energy consumption and consequently reducing CO2
emissions. Kopidou et al. (2017) investigated the common trend and drivers of CO2 emissions
and employment before and after the start of economic crisis, 2000-2007 and 2007-2011,
respectively. They argued that the driving forces behind the environment and the employment
are the economic growth and resource intensity. The higher the economic growth and resource
intensity, the higher the CO2 emissions and employment. These studies based on cross-
sectional dataset while working hours for high and middle income countries showed
differences over time. They have hardly estimated the impact of working hours on ecological
footprints for high and middle income countries separately. Therefore the current study filled
such gap by using panel dataset.
2.8 Growth and energy consumption
The debate over the energy consumption in the process of economic development was
obtained to focus the attention of last few decades to the present time, where the literature of
Chontanawat et al. (2006); Lee (2006); Soytas and Sari (2006); Climent and Pardo (2007); Lee
and Chang (2007); Mahadevan and Asafu (2007); Soytas et al. (2007); Chiou-Wei et al.
(2008); Huang et al. (2008); Jia-Hai et al. (2008); Lee and Chang (2008); Narayan and Smyth
(2008); Bowden and Payne (2009); Nicholas and James (2009); Payne (2009a); Costantini and
Martini (2010); Lee and Lee (2010); Payne (2010b); Ansgar et al. (2011); Ahdi et al. (2013);
33
Ertugrul et al. (2014); Bernard (2015); John et al. (2016) obtained different association
between energy consumption and growth.
However, it is argued that the growth-energy consumption follows the growth,
conservation, neutrality and feedback hypotheses, where the use of energy leads to increase
economic growth as argued by the supporters of growth hypothesis. The conservation
hypothesis argues that the implementation of conservation policies for the reduction of energy
consumption and waste don’t lead to adverse effect on economic growth due to other factors
like political instability, lack of proper infrastructure and particularly mismanagement of
resources lead to increase inefficiency. The neutrality hypothesis argues that the impact of
energy consumption on economic growth is insignificant both in short and long run while the
feedback hypothesis has the views that unidirectional causality from energy consumption to
economic growth is presented in short run while the bidirectional causality is presented in long
run between energy consumption and economic growth because the policies for the energy
efficiency does not lead to adverse impact on economic growth (Squalli, 2007; Ansgar et al.,
2011; Nasir and Rehman, 2011; Apergis and Ozturk, 2015; Ahmad et al., 2017).
34
2.9 Contribution of the study
From the last few decades overexploitation of fisheries, crops, grazing land, livestock and
pollution to fresh water by industries and urbanization increased the ecological footprints and
consequently declined vertebrates population by 58% and marine fishes by 36% of the globe
(GFN, 2014,2016). It implies that the earth is in the age of ecological overshooting where earth
footprint is 54 % greater than its biocapacity. Majority of high income countries and emerging
economies for example China and India have six times larger per capita footprint than the globe
biocapacity of 1.7 gha due to increase use of fossil fuels and energy intensive goods and
services (GFN, 2016a). This implies that high and middle income countries have put
disproportionate pressure on nature while low income countries are trying to meet its basic
needs. Similarly the world’s ecological footprint reached to 1.7% in 2011 and china’s footprint
was 2.5 gha in the same year larger than its biocapacity of 0.9 gha (GFN, 2015). The high
economic growth of China achieved through more consumption of natural resources where its
total ecological footprint reached to 3.9 billion hectares in 2011 while it was 2.6 billion hectares
in 1997, increased by 51% (Wei et al., 2015). Similarly high income countries externalized
their environmental degradation and consequently suppressed well-being and quality of life of
low income countries. The ecological footprint of high and middle income countries was 5 and
2.6 global hectares per person and their biocapacity was respectively 3 and 2.3 global hectares
per person followed by 2 and 0.3 global hectares per person deficit (GFN, 2014, 2015). All
this shows that if everyone follows the consumption pattern and lifestyle of high income
countries, it would require 3 earths and 1.5 earths when follows the lifestyle and consumption
pattern of middle income countries (GFN, 2015). In future the imbalances between demand
and supply of resources as depicted in Appendix B pronounced in different regions of the
world, and rising population, income, urbanization, fossil fuel consumption could lead to
increase demand for cropland, fisheries, grazing, forest and CO2 footprints while climate
change and resource scarcity would disrupt the biocapacity of the globe.
35
The present study empirically contributed from three aspects in the literature of ecological
economics. Firstly, as previous studies have hardly differentiated trend in ecological footprints,
economic growth and ecological efficiency, while in this study, we estimated trend, ecological
efficiency index and the gap between maximum and mean level of ecological efficiency in
resource use, which provided policy guidance regarding environmental degradation to people,
planners, researchers and students who are engaged in environmental field. Secondly, the
existing literature hardly estimated inequality in total ecological footprints and its components,
while in this study; we estimated inequality in the distribution of income, footprints and
environmental impact intensity and highlighted the role of income and environmental intensity
in the computation of the total ecological footprint and its component. Thirdly, the previous
literature of social scientists, environmentalists, sociologists and even ecologists tested
Environmental Kuznets Curve hypothesis and argued that changes in economic growth,
population, political and other socioeconomic factors of different regions could lead to taking
variation in resource use and consequently take environmental variation of the globe.
Based on such argument the current study hasn’t only estimated the EKC5 relationship but
also estimated the ecological overshooting and empirically tested the impact of various driving
forces of the total ecological footprint and its component. Thus the relevance of the current
study is that it provided a guide line to academicians, NGOs, urban planners and governments
regarding inequality, trend of biocapacity, ecological overshooting, socioeconomic variables
and the impact of various driving forces of the total ecological footprint and its component in
case of high and middle income countries.
5 Environmental Kuznets Curve describes environment-growth nexus, where environmental degradation at the
initial stage of economic development increases up to a point, reaches a peak, and then declines with further
economic development (Knight et al., 2013).
36
CHAPTER THREE
THE THEORETICAL BACKGROUND
3.1 Introduction
The expansion of the modern world system that resulted in rapid technological growth has
tightened the impact of factors including population, economic growth, urbanization, market
expansion, industrialization on resource consumption and environmental degradation came
forward from natural resource consumption. Natural resources are generally grouped into two
major categories: renewable and non-renewable natural resources. Renewable resources are
those resources that are capable of regenerating themselves within a relatively short period,
provided the environment in which they are nurtured is not unduly disturbed, such as plants,
fish, forests, soil, solar radiation, wind, tides, and so on (GFN, 2014,2016). Non-renewable
resources are resources that either exist in fixed supply or are renewable only on a geological
time scale, whose regenerative capacity can be assumed to be zero for all practical human
purposes. These resources include metallic minerals like iron, aluminum, copper, and uranium;
and non-metallic mineral like fossil fuels, clay, sand, salt, and phosphates. Human economy
depends on the natural environment for factor of production; assimilate waste and consumption
of amenities as depicted in Fig.3.1:
Figure 3.1
A circular flow of factors of production, environment and economy
37
The next section elaborated the theoretical approach to identify the channels through which
various factors are influencing the ecological footprint/environment of globe and an empirical
literature was followed by chapter 4 of the study.
3.2 The theoretical perspective of Neo-Malthusian:
economic growth and environment
The Malthusian theory has been focusing on the relationship between population,
environment and natural resources (Ahmed, 2004). Malthus predicted severe food shortages
as the population grows geometrically and food grows arithmetically. Malthus focus was only
on food and population, but with the passage of time the Malthusian theory has confronted
with several refinements over time. The neo-Malthusians due to criticisms raised by both
economists and ecologists on Malthusian theory developed their conceptual model
incorporating population, resources, and technology along with human institutions for
environmental sustainability. The theory of neo-Malthusian argued that increased human
activities would lead to increasing stress on the functioning of the environment and in so doing
ultimately lead to environmental degradation. This outcome could arise either from generating
too much waste into the environment or exploiting resource consumption, such as overfishing,
large-scale deforestation, grazing and urbanization. If these outcomes are not controlling then,
it will eventually place bounds on the growth of human activity. In order to incorporate the
fundamental positions of neo-Malthusian regarding the key determinants of environmental
degradation, the (Commoner et al., 1971; Ehrlich and Holdren, 1971) is introduced as:
𝐼 = 𝑃 ∗ 𝐹 − − − − − (3.1)
Here I is the total environmental effect or damage, measured in some standard units. It can
be expressed in a variety of ways, such as overfishing; deforestation and the amount of waste
discharged into the environment yearly. The variable P is population and it is assumed that
more masses lead to more environmental damage i.e.𝜕𝐼 𝜕𝑃⁄ > 0.
38
The variable F is index that measured the per capita environmental impact and can be
expressed
𝐹 =𝐼
𝑃− − − − − (3.2)
The index F is per capita ecological footprint and this is a very important variable and
provides interesting insights when it is discussed in combination with other variables, such as
per capita consumption or income, and the technology by which inputs and outputs are
processed (Ahmed, 2004).
The equation (3.1) exactly states that total environmental impact equal population
multiplied by average impact that each person has on the environment. The full insight of this
equation can be obtained when examining various effects of other variables on the ecological
footprint of an average person. Therefore per capita ecological footprint has expressed by
Ahmed (2004) as:
𝐹 = 𝑓 (𝑃, 𝑦, 𝑔) − − − − − (3.3)
Where y is per capita income GDP of a nation at an aggregate level is expressed as
𝑦 =𝑌
𝑃− − − − − (3.4)
The general assumption regarding variable y in eq. (3.3) states that holding other factor
constant increase in per capita income leads to not just consumption of goods and services but
also to ecological footprint i.e.𝜕𝑓 𝜕𝑃⁄ > 0.
Ehrlich and Holdren (1971) argued that environmental impact increases for two reasons.
First, the size of the population will increase. Second, the impact on the ecological footprint of
more people will also increase. The argument is that if another factor held constant, the
successive addition of people would need increasing use of resource consumption, such as
forest, water, grazing land, energy and other renewable and non-renewable resources. Thus,
by adding more population the per capita impact in term of ecological footprint and
39
environmental degradation would increase successively. Ehrlich and his followers would
contend that rising human population is the predominant factor in accelerating pollution and
other resource problems. According to this model, the impact of population growth on the
environment has primary and secondary impacts in the same direction – suggesting that the
negative impact of population growth is far greater than what it may appear to be when only
factors associated with the primary impact are considered (Fig 3.2).
Figure 3.2
A graphical illustration of Ehrlich’s model.
The channel through which the relationship between change in per capita income (GDP/P)
and ecological footprint is also explained by Commoner et al. (1971); Ehrlich and Holdren
(1971). If other things held constant, an increase in per capita income would change, the
consumption of goods and services. It would increase ecological footprint and consequently
increase environmental damage i.e.𝜕𝑐 𝜕𝑦⁄ ,𝜕𝑓
𝜕𝑐⁄ > 0. Increase in per capita consumption has an
effect on the environment that is independent of population increases as depicted in Figure 3.3.
40
Figure 3.3
Per capita consumption and its effect on the environment.
In the Ehrlich–Commoner model the effect of other factors like technology is captured by
the variable g (Equation 3.3). The effect of technology on ecological footprint is positive due
to inappropriate applications of modern technologies in the extraction, production and
consumption sectors of the economy. This is because technological choices are often made
purely on the basis of profitability considerations rather than environmental sustainability.
In general, the Ehrlich–Commoner model suggest that neo-Malthusians would tend to
claim that the steady increases in population and per capita consumption and the proliferation
of products that are harmful to the environment are the three major factors contributing to
continued global environmental degradation.
3.3 The theoretical perspective of neoclassical economists:
economic growth and environment
The neoclassical economists Grossman and Krueger (1995) and other including the World
Bank (1992) pointed out the role of economic growth in generating pollution/CO2 emissions
through three possible outcomes. The first was an increase in scale of current production, the
second regarding a change in composition of current production and the third consists of a shift
in production techniques. The first factor naturally leads to more pollution in the face of
41
economic growth those results from free trade. The second has ambiguous effects in any
particular country, but could not result in a reduction in pollution everywhere. This leads to
the possibility of pollution. Only the third factor points to the possibility of lower pollution
levels being associated with economic growth (Anders and John, 2009).
Neoclassical theory suggests that the relationship between economic growth and
environmental degradation follow an inverted U-shaped pattern similar to that found by
economist Kuznets (1955) for income inequality. The idea of an Environmental Kuznets Curve
(EKC suggests that as countries modernize, they first pass through a dirty stage marked by
growing ecological impacts; however, they eventually gain the capacity to solve environmental
problems generated by the development process. As countries grow more affluent, increases
in both demand for better environmental conditions and supply of resources that can be devoted
to solving environmental problems (Trainer, 1990; Komen et al., 1997; Anders and John, 2009;
Aslanidis and Iranzo, 2009).
Figure 3.4
The environmental Kuznets curve
42
The vertical axis measures increasing levels of environmental damage. Part A of the figure
3.5 suggests that after a country attains a certain level of per capita income I0, increased income
is associated with lower environmental damage or higher environmental quality. Part B of the
figure suggests that the positive association between income growth and higher environmental
quality does not hold indefinitely. Beyond income level I1, increase in income would lead to
increasing deterioration of the environment.
3.4 Ecological modernization perspective
Over the last two centuries the effect of modernization in form of industrialization,
economic growth, urbanization, trade openness and technological advancement on the globe
is unique in the human history. And there are two side's opinions of the consequences of
modernization on the globe. On one side are those who pointed out that modernization is anti-
ecological and incapable to maintain sustainability. Scholars of this side like neo-Marxian and
Human ecological perspective argue that modernization for the system of capitalism will lead
to environmental degradation when the developed nations further move to industrialization,
urbanization, unequal trade relations and market expansion as we observed it in the last two
decades (Bunker, 1984; Rees, 1992; Dietz and Rosa, 1994; Mol, 1997; Mol and Spaargaren,
2000; York and Rosa, 2003; Rosa et al., 2004; York et al., 2004; Jorgenson, 2005; Dietz et al.,
2007; Jorgenson and Burns, 2007).
On the other side the German political sociologist Huber 1970 in the late 1970s and the
work of Mol (1997) pointed out that modernization lead to ecological sustainability at a globe.
They argue that the continuation of modernization in form of industrialization, economic
development, market expansion and urbanization is the best and perhaps the only way to
achieve ecological sustainability. The view of ecological modernization theorists against the
neo-Marxian and Human ecological perspective is that modernizations lead to anti-ecological
43
and have negative consequences on the globe, where the central principal of EMT is a
modernization greatly concern with ecological rationality. Although at the early stage of
economic development it is not possible to obtain solution for ecological problems, but
progress in economic development will try to mitigate the ecological problems by developed
nations. The rise of the service economy, further modernization in existing institutions and
urbanization, expansion of political rights and civil liberties, and state environmentalism are
all expected to help curb environmental impacts (Mol, 1997; Mol and Spaargaren, 2000). The
consumption-based environmental impacts of a nation through ecological modernization will
decrease. The developed nations will try to decouple the relationship between economic
development and environmental degradation through further development and so less-
developed countries will eventually mitigate consumption-based environmental impacts on
globe (Jorgenson and Clark, 2011).
Figure 3.5
The EMT channel of modernization regarding declining in
environmental Damage/ ecological sustainability
44
3.5 World system and treadmill production perspectives
The cross-sectional analyses regarding a nation’s ecological footprint and economic
development are consistently indicated that total as well as per capita ecological footprints are
largely a function of the level of economic development, usually measured as per capita Gross
Domestic Product. But such relationship is not curvilinear as commonly argued by neo-
classical and ecological modernization theorists. It supported the arguments of world-system
theory and Treadmill of production. These run counter to the ecological modernization theory.
The higher is the level of economic development for more profit accumulation, the higher will
be competition in the global marketplace and consumption of natural resources increased as
argued by the world-system theorists.
The treadmill of production theorists argued that usually producers based in developed
countries and the expansion of products are largely depended on resources which are
commonly extracted from less developed countries. The developed countries externalize
environmental impact by extracting resources of less developed countries and produced
commodities are usually transported to and consume by their population. Increase in economic
development further lead to environmental impact through extraction of natural resources and
waste generated by expansion of production. Thus, according to world-systems theory and
treadmill of production theory, developed countries generate consumption based
environmental impacts that are larger than less-developed countries. And treadmill of
production theory asserts that the effects of resource consumption like fossil fuels would lead
to a negative effect on the total ecological footprint and its components. The area of land
required for cropland, forests, fisheries, grazing land and carbon dioxide will become less and
less when the demand for resources in production process becomes larger and larger.
45
3.6 Export dependence perspective
The theory of export dependence is mainly concerned with the negative consequences of
uneven trade relationship for less-developed countries. The theory asserts that the economic
structure of these countries is based on the export of raw materials which lead to make
exporting countries most vulnerable in the world market, allow the developed countries with
whom they trade get favorable term of trade. The export dependence of this form could lead to
ecological consequences in the form of depletion of cropland, grazing land, forest and
emissions of carbon. They argue that raw materials, agriculture goods and produced
commodities are exported to higher consuming countries on higher on the basis of their
international power which leads to uneven ecological exchange. The export intensity is a
negative effect on a nation’s ecological footprint (Jorgenson and Burns, 2007). The export
dependency theorists assert that the effect of export intensity on ecological footprint is
negative.
46
CHAPTER FOUR
DATA AND METHODOLOGY
4.1 Introduction
This chapter explains data sources and methodology to construct ecological efficiency,
environmental impact intensity, Atkinson index of ecological footprints and specification of
appropriate econometric model.
4.2 Data
To achieve objectives of the study, the quarterly data6 from 2003q1-2011q4 are established
for the panel data analysis. The separate and combine panel models of 35 high and 77 middle
income countries7 are established, defining the total ecological footprint and its components as
the dependent variables. Data used in this study are drawn from various sources including
World development indicators, Global footprint network, the conference board and
international energy statistics. The dataset of ecological footprint derived from the GFN is one
of the international organizations. It documents the ecological footprint by dividing the yearly
consumption of cropland, forest, grazing land, fishing grounds, CO2 footprint and built-up land
activities from the production of land expressed in hectares and this ratio is multiplied by the
yield and equivalence factors derived by the GFN. In the second stage all the area of land
required for cropland, forest, grazing land, fishing grounds, CO2 footprint and built-up land
aggregates in form of total ecological footprint global hectares in a given year. At every stage
of computation process of ecological footprint, the double counting is avoided in order to
improve accuracy of environmental impact indicator i.e. the total ecological footprint. This is
6 Due to the limitation of data availability on the dependent variables, we therefore, converted the annual data into
the quarterly data and re-estimate the separate and combine panel of high-middle income countries as suggested
by external examiner. 7 Appendix E consists of the list of high-middle incomes countries.
47
a comprehensive measure because raw input data for the computation of national’s ecological
footprint derived from different sources for example Food and Agriculture Organization (FAO,
International Energy Agency IEA, United Nations Commodity Trade Statistics Database UN
COMTRADE, World Development Indicator Database WDI, The conference board, Central
for Sustainability and the Global Environment SAGE and other Databases (Galli et al., 2012).
The GFN covered 152 countries, different regions and the World for estimation of their
ecological footprint at irregular basis from 1960 to 2012 with two or five year intervals.
The data on working hours is obtained from the conference board. It is a non-profit business
membership and research group organization. It counts approximately 1200 public and Private
Corporation and other organization and 5 regions i.e. Asia, China, Europe, Middle East and
United dataset on working hours, employment and consumer confidence.
The data on fossil fuel, coal, and oil and gas consumption are obtained from the conference
US international energy statistics. In is an international organization collected data on energy
consumption of national level. The data on livestock and fish production are obtained from the
Food and Agriculture Organization (FAO) for different periods. The data on other variables
are obtained from the World Bank dataset.
48
The definition, unit of measurement and data sources regarding different variables of the
study elaborated in the following Tables.
Table 4.1
Dependent Variables
Dependent
Variables
Definition Measurement
Unit
Data
Source
Data Period
Ecological
Footprint
The area of land required to support
humanity demand and to assimilate CO2
emissions as well as waste generated by
human activities
Global Hectares:
gha
Global
Footprint
Network:
GNF,
www.foot
printnetwo
rk.org/
2003q1-2011q4
Carbon
Footprints:
CARF
The area of land required for
assimilation of CO2 emissions and
Waste generated by human activities
Cropland
Footprints:
CROF
The area of land required to produce
crop activities
Fisheries
Footprint :
FISHF
The area of land required for fisheries
activities
Forest land
Footprint :
FORESTF
forest ecological footprint quantifies the
area required to produce the forest
products
Grazing
land
Footprint:
GRAZF
grazing ecological footprint quantifies
the land requirement for grazing
activities
Built-up
land
Footprint :
BUILTF
built-up ecological footprint is a
measure of land requirement to adjust
urbanization activities
49
Table 4.2
Explanatory Variables
Explanatory
Variables
Definition Measurement
Unit
Data Source Data Period
Population: PoP Total population Million Word Bank
2003q1-2011q4
Economic
Growth : Yg
Per capita Gross Domestic
product
US$ in
Purchasing
Power Parity:
2000 prices
Word Bank
Urbanization :
UR
The percentage of total pop.
living in urban areas, centered by
subtracting the mean of the log
of percentage urban and then
squared to reduce collinearity
with percentage urban
% of total
Pop.
Word Bank
Fossil Fuels : FF Fossil fuels energy consumption
as percentage total
% Word Bank
Export Intensity
:EI
Export as a percentage of Gross
Domestic Product
% Word Bank
Manufacturing
Intensity: MI
Manufacturing as a percentage of
Gross Domestic Product
% Word Bank
Service Intensity:
SI
Service as a percentage of Gross
Domestic Product
% Word Bank
Agriculture
Intensity: AI
Agriculture as a percentage of
Gross Domestic Product
% Word Bank
Term of Trade:
TOT
Export and Import as a
percentage of Gross Domestic
Product
% Word Bank
Coal Consumption of coal Tons IES
Oil Consumption of oil Barrels IES
Gas Consumption of gas Million cubic
feet
IES
50
Continued…
Explanatory
Variables
Definition Measurement
Unit
Data Source Data Period
Hours of work
:HW
Annual worked hours per
employee
Hrs The
Conference
Board
2003q1-2011q4
Fish intensity:
SF
Share of fish export as a
percentage of total export
% Word Bank
& FAO
Education: EDU Education expenditure as a
percentage of Gross
Domestic Product
% Word Bank
& FAO
Export of
Merchandize
goods: CA
Export of merchandize goods
as a percentage of Gross
Domestic Product
% Word Bank
& FAO
Export of
primary
goods:EP
Export of primary goods as a
percentage of total export
% Word Bank
& FAO
Income
inequality: IE
Gini Coefficient, the
distribution of income within
countries
% Word Bank
& FAO
Employment to
population ratio:
EM
Employment divided by
population
% Word Bank
& FAO
Crop and
livestock
products
Export of crop and livestock
products
Million tons FAO STAT
51
4.3 Methodology
It is concluded from the discussion of the previous chapter that ecological footprint is
affected by different factors like economic growth, population, urbanization, technology and
fossil fuels. However, in this section we built a theoretical framework from the neo-Malthusian
perspective that focused on the relationship between population, footprints and environmental
degradation and therefore the Ehrlich and Holdren (1971) can be written as:
𝐼𝑡 = 𝑃𝑡 ∗ 𝐹𝑡 −−−−− (4.1)
Where I is the total environmental damage or total ecological footprint; P is the population
and assume that increase in population leads to increase ecological footprint i.e.𝜕𝐼 𝜕𝑃⁄ > 0. The
variable F is per capita ecological footprints (Ahmed, 2004; White, 2007).
𝐹𝑡 =𝐼𝑡𝑃𝑡−−−−− (4.2)
The per capita and total ecological footprints vary from countries to countries and from
region to region because of difference in its standard of living, population and other
socioeconomic factors (Ahmed, 2004; Jorgenson and Burns, 2007; Knight et al., 2013).
However, the ecological footprint has expressed by Ahmed (2004); Knight et al. (2013):
𝐹𝑡 = 𝑓 (𝑃𝑡, 𝑌𝑡, 𝑇𝑡, 𝑍𝑡) − − − − − (4.3)
The ecological footprint is linked with economic output and whether economic output is
achieved in efficient way or not when using resources. In order to obtain ecological efficiency
let assume that economic output is denoted by Y is a function of capital, labor and other
resource use and let ecological footprints is a function of population, affluence, technology and
other factors:
𝑌𝑡 = 𝑓(𝐾𝑡, 𝐿𝑡, 𝑋𝑡) − − − − − − − (4.4)
𝐹𝑡 = ɸ(𝑃𝑡, 𝑌𝑡, 𝑇𝑡, 𝑍𝑡) − − − − − − − (4.5)
52
Let ecological efficiency through the definition of Qiu (2013); (Wei et al., 2015) can be
expressed as:
𝐸𝐸𝑡 =𝑌𝑡𝐹𝑡 − − − − − − − (4.6)
Where 𝐸𝐸𝑡 is ecological efficiency; 𝑌𝑡 is economic output and 𝐹𝑡 is environmental
affluence (Ecological footprint in year t. Since, economic output is determined by factors of
production and other inputs as captured by vector X (Ahmed, 2004; Anders and John, 2009;
Jalil and Feridun, 2011; Gulden and Mehmet, 2014), while ecological footprint is determined
by population, affluence, technology and other factor as captured by vector Z (Knight et al.,
2013). The ecological efficiency can be expressed as:
𝐸𝐸𝑡 =𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)
ɸ(𝑃𝑡, 𝑌𝑡, 𝑇𝑡, 𝑍𝑡) − − − − − (4.7)
𝐸𝐸𝑡 =𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)
ɸ(𝑃𝑡, 𝑇𝑡, 𝑍𝑡 , 𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)) − − − − − (4.8)
The last equation (4.8) states that economic output is influenced by capital (K), labor (L)
and other factors like energy consumption, foreign direct investment, term of trade, fiscal
decentralization , institutional quality, inequality etc. captured by vector X while the ecological
footprints depends on population, affluence, technology and other factors like export intensity,
inequality, urbanization, fossil fuel, military expenditure, work hours etc. captured by vector
Z (Jorgenson and Burns, 2007; Knight et al., 2013). However, ecological efficiency shows the
ability to produce economic output with less resources use and pollution output expressed by
ecological footprint and varies due to changes in factors compositions (Anders and John,
2009). To estimate the ecological efficiency the procedure of Qiu (2013); Wei et al. (2015)
followed, where they used GDP as an indicator for economic output and total ecological
footprint as consumption based environmental indicator.
53
The estimation of ecological efficiency through the above process reveals that whether the
efficiency of resource utilization increases, decreases or remain constant in the given period.
In other words, it shows trend analysis of ecological efficiency. In this study, we also compared
the ecological efficiency of the current year with previous year ecological efficiency of
resource utilization through the constructed ecological efficiency index. The procedure we
adopted based on Qiu (2013); Wei et al. (2015) methodology and ecological efficiency index
can be expressed as:
𝐸𝐸𝐼𝑡 =𝐸𝐸𝑡𝐸𝐸𝑡−1
− − − − − (4.9)
Where, 𝐸𝐸𝐼 index of ecological efficiency, 𝐸𝐸𝑡 is the ecological efficiency of current year
t and 𝐸𝐸𝑡−1 is previous year ecological efficiency and since we use the ecological footprint as
a resource consumption indicator and income as a value of nation’s product. The ecological
efficiency index can be computed:
𝐸𝐸𝐼𝑡 =𝐹𝑡−1
𝐹𝑡.𝑌𝑡
𝑌𝑡−1 − − − − − (4.10)
𝐸𝐸𝐼𝑡 =ɸ(𝑃𝑡−1, 𝑇𝑡−1, 𝑍𝑡−1, 𝑓(𝐾𝑡−1, 𝐿𝑡−1, 𝑋𝑡−1))
ɸ(𝑃𝑡, 𝑇𝑡, 𝑍𝑡 , 𝑓(𝐾𝑡, 𝐿𝑡, 𝑋𝑡)).
𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)
𝑓(𝐾𝑡−1, 𝐿𝑡−1, 𝑋𝑡−1) − − − − − (4.11)
The interpretation of equation (4.11) is straightforward because the value of EEI is greater
than one indicates reduction in the amount of energy or resource consumption and the amount
of pollution emitted per unit of economic output in year t is lower than year t-1. If EEI less
than one, meaning that a country’s resources are used recklessly for generating more income
and economic growth. In other words, a country is achieving its economic growth at the cost
of environmental degradation (Anders and John, 2009). However, it is necessary that whether
there exists the gap between country efficiency in resources utilization and its maximum (best
54
performer) across the countries (Qiu, 2013; Wei et al., 2015). And therefore we followed
definition and computation process of Qiu (2013) as:
𝑅𝐼𝑡 =𝑚𝑎𝑥(
𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)
ɸ(𝑃𝑡,𝑇𝑡,𝑍𝑡,𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)))
(𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)
ɸ(𝑃𝑡,𝑇𝑡,𝑍𝑡,𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)))
≥ 1 − − − − − (4.12)
The value of 𝑅𝑡 reflects the gap between anefficiency in resources utilization and maximum
ecological efficiency for year t in group of nations because its value equal to one shows
efficiency in resources utilization is maximum (best performer and 𝑅𝐼𝑡greater than one implies
that efficiency in resources utilization is less than maximum ecological efficiency and have
more room of potential to achieve maximum level of efficiency. However, environmental
impact intensity could be estimated whenever computed ecological efficiency because to
obtain maximum level of ecological efficiency (best performer depends on resource
consumption and consequently increase environmental degradation (York et al., 2004; Juan
and Jordi, 2013).
𝑇𝑡 =𝐸𝐹𝑡
𝑌𝑡 − − − − − (4.13)
𝑇𝑡 =ɸ(𝑃𝑡, 𝑇𝑡, 𝑍𝑡 , 𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡))
𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)− − − − − (4.14)
Where, T shows the amount of energy or resources consumption and the amount of
pollution omitted per unit of economic output and from the introduction it is clear that because
of inequality in total ecological footprint and its component the different regions of the world
have different ecological overshooting and environmental degradation. Therefore, we also
estimated inequality in distribution of resources use i.e. total ecological footprint and its
components.
55
4.3.1 Atkinson Index of ecological footprint inequality based on
environment intensity and per capita income
In this section we estimated the role of environment intensity and per capita income in
ecological footprint inequality through the methodology of White (2007); Juan and Jordi
(2013) , where they used the Atkinson (1970) index of inequality by incorporating ecological
footprint as:
𝐴𝐹 = 1 −𝐹𝑒𝜇𝐹−−−− − (4.15)
Where AF is the Atkinson index of inequality, Fe is equally distributed of footprint and 𝜇𝐹
is the mean of ecological footprint. The index value of Atkison index is ranging zero to one. If
resources are equally distributed i.e F1=F2=F3=…..=Fn, the AF will be zero and will be one in
case of completely inequality in ecological footprint. According to White (2007); Juan and
Jordi (2013), where the ecological footprint can be defined as:
𝐹𝑖 = 𝑃𝑖 ∗ 𝑦𝑖 ∗𝐹𝑖
𝑌𝑖−−−− − (4.16)
Where Fi is the ecological footprint of country i, Pi is its population, yi is its per capita
income. The per capita ecological footprint can be expressed as:
𝐹𝑖 = 𝑦𝑖 ∗ 𝑤𝑖 −−−− − (4.17)
Where Fi is per capita ecological footprint of country i and wi is the environmental impact
intensity (or ecological efficiency. By using definition of White (2007); Juan and Jordi (2013)
the Atkinson index can be expressed as:
𝐴𝐹 = 1 −∏{𝐹𝑖𝜇𝐹}
1𝑝𝑖
𝑛
𝑖=1
−−− −− (4.18)
56
Where 𝜇𝐹is mean value of per capita ecological footprint and pi is the relative population
of country i. After manipulating and substituting equation (4.17) into equation (4.18), the index
can be expressed as:
1 − 𝐴𝐹 =∏{𝑦𝑖 ∗ 𝑤𝑖𝜇𝐹
}1/𝑝𝑖
𝑛
𝑖=1
− −−−− (4.19)
1 − 𝐴𝐹 = {𝜇𝑦𝜇𝑤
𝜇𝐹}∏{
𝑦𝑖 ∗ 𝑤𝑖𝜇𝑦 ∗ 𝜇𝑤
}
1/𝑝𝑖𝑛
𝑖=1
−−−−− (4.20)
1 − 𝐴𝐹 = {𝜇𝑦𝜇𝑤
𝜇𝐹}∏{
𝑦𝑖𝜇𝑦}
1𝑝𝑖
𝑛
𝑖=1
∏{𝑤𝑖𝜇𝑤}
1𝑝𝑖
𝑛
𝑖=1
−−−−− (4.21)
1 − 𝐴𝐹 = {𝜇𝑦𝜇𝑤
𝜇𝐹} (1 − 𝐴𝑦) ∗ (1 − 𝐴𝑤) − − − − − (4.22)
Where µy is mean value of per capita income and µw is mean value of environmental impact
intensity. The Atkinson indices of ecological footprint, income and environmental impact
intensity are shown by AF, Ay and Aw. The value of 1-Ai indicates an Atkinson measure of
equality where perfect equality would be equal to one and complete inequality would be equal
to zero by White (2007). Thus the interpretation of equation (4.22) is straightforward. The
Atkinson index (1-AF) of ecological footprint depends on distribution of income and
environmental impact intensity (ecological efficiency, and the means of these variables. It is
commonly argued that domestic income inequality is inversely related to a nation’s ecological
footprint. The argument is that nations with higher income inequality would have low per
57
capita ecological footprint because they have relative lower income and mainly focus on export
of raw material and agriculture commodities (White, 2007; Jorgenson, 2009; Juan and Jordi,
2013). However, in this study we estimated and compared Atkinson index of footprints,
environment intensity and per capita income of high and middle income countries.
4.3.2 Empirical specification of various influencing factors
of ecological footprint and its components
In this study we used the STIRPAT model developed by Dietz and Rosa (1994) and further
explored by York and Rosa (2003), by Rosa et al. (2004) and by Dietz et al. (2007). The
STIRPAT model is the reformulation of Ehrlich and Holdren (1971) IPAT where population
(P), affluence (A) and technology (T) are the influencing factors of environment (I). The
coefficients associated with influencing factors show elasticity because the model is a
multiplicative function of population, income and technology (York and Rosa, 2003; Anders
and John, 2009; Knight et al., 2013). However, the basic STIRPAT model used in this study
for empirical testing of neo-classical economists and neo-Malthusian view regarding
environmental impacts of economic growth, population and other explanatory variables known
as the Stochastic Regression Impact of Population, Affluence and Technology (STIRPAT)
model and can be expressed as:
𝐸𝐹𝑖 = 𝛼𝑃𝛽𝑖𝑌𝛾𝑖𝑇𝛿𝑖휀𝑖 −− −−− (4.23)
Where EF=Ecological Footprints, P=population, Y=economic growth, T=Technology and
ε =error term. The constant “α” scales the model, whereas β, γ and δ are exponents of P, Y and
T respectively. To find the relationship between ecological footprint and its influencing factors,
the nonlinear form of equation (4.23) can be converted to a linear form after ln transformation,
because it is easier to work with linear equations rather than nonlinear equations (Ahmet and
58
Sevil, 2015). T is incorporated into error term because of no clear agreement on valid
technology indicators. The nonlinear equation of STIRPAT model after this modification
becomes:
𝑙𝑛(𝐸𝐹) = 𝑎 + 𝛽 ln(𝑃) + 𝛾𝑙𝑛(𝑌) + 𝜇𝑖𝑡 −−−−− (4.24)
The coefficients associated with independent variables are elasticities that show a
percentage change in the dependent variable due to one percentage change in the independent
variable by holding the effects of other factors constant. The various influencing factors of
dependent variable could be incorporated in the STIRPAT model (Knight et al., 2013).
However, in environmental social science the influencing factors of ecological footprint used
are population, economic growth and working hours. The effect of work hours on ecological
footprint was further disaggregated into hours of work per employee, labor productivity and
employment to population ratio by Anders and John (2009); Knight et al. (2013) to test
hypothesis that longer working hour is responsible factor for environmental degradation. The
studies like York and Rosa (2003); Cole (2004); Rice (2007) disaggregated population into
urbanization (i.e. proportion population living in urban area) to test the hypotheses regarding
its effect on environment, emissions, footprint and energy consumption where they found a
positive relationship because of the increase in urbanization in form of single family and high
rise building demand for energy use .
However, the studies like Liddle (2004); Fan et al. (2006) found a negative relationship
between urbanization, energy consumption and CO2 emissions in case of high income
countries because of green technology use for urban activities like electrical transportation
system through which CO2 emissions reduced. The number of studies however, decomposed
the term technology into manufacturing and service’s share of GDP and found a positive effect
on energy consumption while a negative relationship between service and environment
59
degradation was observed (Dietz et al., 2003; York and Rosa, 2003; Fan et al., 2006;
Mahadevan and Asafu, 2007; Kaneko and Poumanyvong, 2010). However, Kaneko and
Poumanyvong (2010); Perry (2014) in STIRPAT model decomposed population into
urbanization and found that higher economic activities associated with urbanization and leads
to increase income of urban residents demand more for energy intensive products like
automobile, air conditioning etc. which increases CO2 emissions, and also added that higher
wealthier nations also care of environment and try to use environmental friendly technology
products. In this study we advanced the STIRPAT model in a number of ways as we
disaggregated the dependent variable into total ecological footprint and its components (i.e.
cropland, forest, grazing, and fisheries, built-up land and CO2 footprint). We estimated the
effect of various influencing factors of each component of footprint by decomposing the basic
influencing factors of STIRPAT model and tested the EKC, modernization, world-system and
export dependency hypotheses. Our first dependent variable is the ecological footprint that
represents total area required to produce the fibers and food, sustain energy consumption, and
give space for infrastructure of a given nations/locality. It shows consumption-based pressure
on environment measured in global hectares per person and is widely used indicator in the
environmental social science (Richard et al., 2003; York and Rosa, 2003; Jorgenson, 2005;
Jorgenson and Burns, 2007; Knight et al., 2013). The empirical specification of influencing
factors of total ecological footprints can be expressed as:
𝐸𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡 , 𝑌2𝑔𝑖𝑡 , 𝑃𝑂𝑃𝑖𝑡 , 𝑈𝑅𝑖𝑡 , 𝐹𝐹𝑖𝑡 , 𝐸𝐼𝑖𝑡 , 𝑆𝐼𝑖𝑡,𝑀𝐼𝑖𝑡 , 𝐴𝐼𝑖𝑡 , 𝐼𝐸𝑖𝑡 , 𝜔𝑖𝑡)−−−−− (4.25)
+ - + + + + + - - -
The second dependent variable is energy (Carbon ecological footprint) quantifies the
carbon emissions global hectares per person. The functional relationship between carbon
footprint and its relevant influencing factors is expressed as:
60
𝐶𝐴𝑅𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡 , 𝑌2𝑔𝑖𝑡 , 𝑃𝑂𝑃𝑖𝑡 , 𝑇𝑂𝑇𝑖𝑡 , 𝑈𝑅𝑖𝑡 , 𝐶𝑂𝐴𝐿𝑖𝑡 , 𝑂𝐼𝐿𝑖𝑡 , 𝐺𝐴𝑆𝑖𝑡 ,𝑀𝐼𝑖𝑡, 𝐻𝑊𝑖𝑡 , 휀𝑖𝑡)−−−−(4.26)
+ - + + + + + ? + ?
The third dependent variable is fisheries ecological footprint is the area required to produce
fish and seafood products in order to fulfill the consumption, accelerate the economic
development and increase share of fish export of a nation. The determinants of fisheries
footprint in its functional form are expressed as:
𝐹𝐼𝑆𝐻𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝑆𝐹𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝜇𝑖𝑡) − − − − − (4.27)
+ - + ? +
The fourth dependent variable is cropland ecological footprint quantifies the area required
of crop that consumed by a country’s population and to feed animals whose production like
meat, eggs, milk etc are consumed in a year. The functional form of cropland footprint and its
determinants is expressed as:
𝐶𝑅𝑂𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝐴𝐼𝑖𝑡 , 𝐸𝐷𝑈𝑖𝑡, 𝐶𝐴𝑖𝑡, 𝜗𝑖𝑡) − − − − − (4.28)
+ - + + + - +
Our fifth dependent variable is forest ecological footprint quantifies the area required to
produce the forest products include all timber products, pulp, paper and paperboard that
consumed by a country’s population and to feed animals whose production like meat, eggs,
milk etc. are consumed in a year. The functional form of forest footprint and its determinants
is expressed as:
𝐹𝑂𝑅𝐸𝑆𝑇𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝐸𝑃𝑖𝑡 , 𝐸𝐷𝑈𝑖𝑡 , 𝐼𝐸𝑖𝑡, 𝜖𝑖𝑡) − − − − − (4.29)
+ - + + + - -
The sixth dependent variable is grazing ecological footprint that quantifies the land
requirement for grazing of livestock that provide consumption of animal products includes
61
meat, dairy products and wool for a given nation in year. The determinants of grazing footprint
are expressed in the following functional form:
𝐺𝑅𝐴𝑍𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝑃𝐿𝑖𝑡, 𝜓𝑖𝑡) − − − − − (4.30)
+ - + + +
Our last dependent variable is built-up ecological footprint is a measure of land requirement
to adjust urbanization, requirement of industrial sectors, services intensity, housing, and
transportation. The various determinants of built-up footprint are expressed in the following
functional form:
𝐵𝑈𝐼𝐿𝑇𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝑀𝐼𝑖𝑡, 𝑆𝐼𝑖𝑡, 𝐸𝑀𝑖𝑡, 𝜉𝑖𝑡) − − − − − (4.31)
+ - + + ? + +
62
4.3.3 The expected theoretical linkages between dependent
and independent variables: EF and economic growth
The expected relationship between economic growth and ecological footprint is positive in
its linear while negative in its square term. If this maintain, it verifies the environmental
Kuznets curve. The environmental impact of population is assumed that more masses lead to
more environmental damage as described by Ehrlich and Holdren (1971).
4.3.3.1 EF and Population
The Ehrlich and Holdren (1971) argued that environmental impact increases for two
reasons. First, the size of population (P) will increase. Second, the impact on ecological
footprint of more people will also increase. The argument is that if other factor held constant,
successive addition of people would need more resource consumption such as forest, water,
grazing land, energy and other renewable and non-renewable resources. Thus by adding more
population the per capita impact in term of ecological footprint and environmental degradation
increase successively. Ehrlich and his followers contend that rising human population is the
predominant factor in accelerating pollution and other resource problems.
4.3.3.2 EF and Urbanization
Impacts of urbanization on the environment are partially and separately discussed in three
relevant theories: ecological modernization, urban environmental transition and compact city
theories. The first theory focuses on impacts at the national level, while the others discuss
impacts at the city level. Ecological modernization theory emphasizes not only economic
modernization but also social and institutional transformations in explaining the effects of
modernization on the environment. In this theory, urbanization is the process of social
transformation regarded as one important indicator of modernization. It is argued that
environmental problems may increase from low to intermediate stages of development.
However, further modernization can minimize such problems, as societies come to realize the
63
importance of environmental sustainability, seeking to decouple environmental impact from
economic growth through technological innovation, urban agglomeration, and the shift toward
knowledge and service based industries (Crenshaw and Jenkins, 1996; Mol, 1997; Anders and
John, 2009; A. Usama et al., 2014).
4.3.3.3 EF and export intensity
Export dependence theory focuses on the negative consequences of uneven trade
relationships, particularly for less-developed countries. The theory asserts that high levels of
export dependence make an exporting country more vulnerable to world-economic market
forces, and allow the developed nations with whom they exchange to obtain favorable terms
of trade. This type of acts creates negative ecological consequences in the form of depletion of
raw materials. Therefore export dependence in the form of export as percentage of GDP would
have negative ecological effect. However, few theories argued that due to financial
development the impact of trade openness on ecological footprint is positive.
4.3.3.4 EF and SI, MI and AI
Some macroeconomic perspectives suggest that shifting from manufacturing, agriculture,
and extractive activities to a more service-based economy offers a potential solution to
reducing the scale and intensity of the environmental impacts of nation-states. By using these
arguments one can test that the service based economies have relatively low ecological
footprint than manufacturing and agriculture economies. But the world-system and unequal
exchange theories claim that the high income nation due to its power can utilize more resources
and even externalize pollution (Jorgenson and Burns, 2007; Knight et al., 2013).
64
4.3.3.5 EF and Domestic income Inequality
Some evidence predicts that domestic income inequality is inversely related to a nation’s
ecological footprint. The argument is that nations with higher income inequality would have
low per capita ecological footprint because they have relative lower income. They mainly
focus on export of raw material, agriculture goods (Jorgenson, 2005; Jorgenson and Burns,
2007).
4.3.3.6 EF and Energy Consumption
Some evidence suggests that energy consumption is positively related to ecological
footprint (Shahbaz et al., 2013a; A. M. Usama et al., 2014). The argument is that the nation
with more strong financial development, trade openness, and urbanization and services based
activities increases the impact of energy consumption on environmental damage. On the basis
of above argument we hypothesized that energy consumption particularly in form of coal, oil
and gas consumption is positively related to ecological footprint.
4.3.3.7 EF and Education
The high literacy rates have positive effect on natural resources consumption. The
argument is that high income nations correspond with high literacy rates increases
opportunities of depletion of resources (Jorgenson et al., 2005).
65
4.4 Analytical tools
This section covered the estimation procedures of ecological efficiency, index of ecological
efficiency, Atkinson index and the econometric modeling for various driving forces of total
ecological footprint and its components in case of high and middle income countries.
4.4.1 The computation of ecological efficiency
The ecological efficiency which is based on the procedure of Qiu (2013); Wei et al. (2015)
estimated as:
𝐸𝐸2003 =∑ 𝐺𝐷𝑃𝑖2003𝑛𝑖=1
∑ 𝐸𝐹𝑖2003𝑛𝑖=1
−− −−− (4.32)
𝐸𝐸2005 =∑ 𝐺𝐷𝑃𝑖2005𝑛𝑖=1
∑ 𝐸𝐹𝑖2005𝑛𝑖=1
−− −−− (4.33)
𝐸𝐸2007 =∑ 𝐺𝐷𝑃𝑖2007𝑛𝑖=1
∑ 𝐸𝐹𝑖2007𝑛𝑖=1
− −−−− (4.34)
𝐸𝐸2009 =∑ 𝐺𝐷𝑃𝑖2009𝑛𝑖=1
∑ 𝐸𝐹𝑖2009𝑛𝑖=1
− −−−− (4.35)
𝐸𝐸2011 =∑ 𝐺𝐷𝑃𝑖2011𝑛𝑖=1
∑ 𝐸𝐹𝑖2011𝑛𝑖=1
− −−−− (4.36)
4.4.2 The Computation of ecological efficiency index
The ecological efficiency index for the period 2005, 2007, 2009 and 2011 with two years
intervals estimated by using the following computation process:
𝐸𝐸𝐼2005 =∑ 𝐺𝐷𝑃𝑖2005𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2003𝑛𝑖=1
∗∑ 𝐸𝐹𝑖2003𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2005𝑛𝑖=1
−−−−− (4.37)
66
𝐸𝐸𝐼2007 =∑ 𝐺𝐷𝑃𝑖2007𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2005𝑛𝑖=1
∗∑ 𝐸𝐹𝑖2005𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2007𝑛𝑖=1
−−−−− (4.38)
𝐸𝐸𝐼2009 =∑ 𝐺𝐷𝑃𝑖2009𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2007𝑛𝑖=1
∗∑ 𝐸𝐹𝑖2007𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2009𝑛𝑖=1
−−−−− (4.39)
𝐸𝐸𝐼2011 =∑ 𝐺𝐷𝑃𝑖2011𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2009𝑛𝑖=1
∗∑ 𝐸𝐹𝑖2009𝑛𝑖=1
∑ 𝐺𝐷𝑃𝑖2011𝑛𝑖=1
−−−−− (4.40)
The ecological efficiency index in this way compares efficiency of resource utilization of
current year with previous year efficiency because value of index greater than one implies that
economic output per units of ecological footprint in current year is greater than previous year.
In other words, reduction in the amount of energy or resources consumption and the amount
of pollution emitted per unit of economic output in current year is lower than previous year.
4.4.3 The Computation of environmental impact intensity
The mean environmental impact intensity of total ecological footprints and its components
estimated as:
𝜇𝑇 =∑ 𝑇𝑡2011𝑡=2003
𝑛−−−− − (4.41)
𝜇𝑇𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− − − −(4.42)
𝜇𝑇𝐶𝑂2 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝐶𝑂2 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− − −− − (4.43)
67
𝜇𝑇𝑐𝑟𝑜𝑝𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝑐𝑟𝑜𝑝𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− − − − − (4.44)
𝜇𝑇𝑔𝑟𝑎𝑧𝑖𝑛𝑔 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝑔𝑟𝑎𝑧𝑖𝑛𝑔 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− − − − − (4.45)
𝜇𝑇𝑓𝑜𝑟𝑒𝑠𝑡 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝑓𝑜𝑟𝑒𝑠𝑡 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛−−−−− (4.46)
𝜇𝑇𝑓𝑖𝑠ℎ𝑖𝑛𝑔 𝑔𝑟𝑜𝑢𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝑓𝑖𝑠ℎ𝑖𝑛𝑔 𝑔𝑟𝑜𝑢𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− −− −(4.47)
𝜇𝑇𝑏𝑢𝑖𝑙𝑡 𝑢𝑝 𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (
𝑏𝑢𝑖𝑙𝑡 𝑢𝑝 𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− − − − − (4.48)
The mean value show per unit environmental intensity of economic output and we
estimated and compared mean environmental intensity of high and middle income countries,
where its environmental intensity indicated different structure. The higher environmental
impact intensity in case of the total ecological footprint and its components leads to explain
higher per unit of environmental impact of economic output because the ecological footprint
is basically the consumption based environmental impact indicator.
68
4.4.4 The Computation of Atkinson index of equality
In this section the Atkinson index of ecological footprint equality estimated by using the
following equation as:
1 − 𝐴𝐹 = {𝜇𝑦𝜇𝑤
𝜇𝐹} (1 − 𝐴𝑦) ∗ (1 − 𝐴𝑤) − − − −− (4.49)
Where
𝜇𝐹 =∑ (
𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )2011
𝑡=2003
𝑛− − − − − (4.50)
𝜇𝑤 =∑ (
𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛− − −− − (4.51)
𝜇𝑦 =∑ (
𝐺𝐷𝑃𝑡𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )2011
𝑡=2003
𝑛− −−−− (4.52)
(1 − 𝐴𝑤) =∏
{
(𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡
𝐺𝐷𝑃𝑡⁄ )
𝑖
∑ (𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡
𝐺𝐷𝑃𝑡⁄ )2011
𝑡=2003
𝑛 }
1/𝑝𝑖
−−− −− (4.53)
𝑛
𝑖=1
(1 − 𝐴𝑦) =∏
{
(
𝐺𝐷𝑃𝑡𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )
𝑖
∑ (𝐺𝐷𝑃𝑡
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )2011
𝑡=2003
𝑛 }
1/𝑝𝑖
𝑛
𝑖=1
−−−−− (4.54)
69
4.4.5 Econometric modelling
In this section we explained the fixed effects models and random effects models which are
the two Panel estimates approaches and set model selection criteria i.e. the Huasman test.
4.4.5.1 Fixed effect model
The fixed effects model focuses on individual specific effect and assuming time effect on
dependent variable is constant. It implies that intercept of fixed effects model changes cross-
sectional but remains constant over time and the slopes are fixed with respect to both cross-
sectional and over time (Hsiao, 2003; Frees, 2004). We therefore denoted cross-sectional
with the index ith range from 1 to n and the time observation denoted with index tth range
from 1 to T. By using these indices and consider 𝑦𝑖𝑡 is the dependent variable of the ith
county at the tth time point which depends on K exogenous variables, zit,1, zit,2,……..,zit,K of a
𝐾 × 1 column vector (Frees, 2004):
𝑍𝑖𝑡 =
(
𝑧𝑖𝑡,1𝑧𝑖𝑡,2....
𝑧𝑖𝑡,𝐾)
And the data for the ith country arranged through the methodology of (Frees, 2004)as:
(
𝑧𝑖1,1 ,𝑧𝑖1,2 ……𝑧𝑖1,𝐾, 𝑦𝑖1𝑧𝑖2,1 ,𝑧𝑖2,2 ……𝑧𝑖2,𝐾, 𝑦𝑖2
.
.
.
.𝑧𝑖𝑇,1 ,𝑧𝑖𝑇,2 ……𝑧𝑖𝑇,𝐾, 𝑦𝑖𝑇)
70
Where z denoted explanatory variables name, i denoted the ith country, the first numeric
value denoted time period and the second one explained independent variable. Similarly y
denoted dependent variable of the ith country and numeric value connected with it explains
time period from 1 to T. The above expression denoted data arrangement of dependent and
independent variables while the fixed effects model can be expressed as:
𝑦𝑖𝑡 = 𝛾𝑖 + 𝛽/𝑍𝑖𝑡 + 휀𝑖𝑡 −−−−− (4.55) , 𝑖 = 1.2……𝑁
, 𝑡 = 1.2…… . 𝑇
Where 𝛽/ is a 1 × 𝐾 vector of constant (𝛽1, 𝛽2,…… . . , 𝛽𝐾) and 𝛾𝑖 is 1 × 1 scalar constant
represents the effect of variables that are individual specific effect and more and less constant
over time. The error term 휀𝑖𝑡 is captured the irregular effect of omitted variables for both
individuals and time periods with mean zero and variance 𝛿2 . The fixed effect model in
vector system can be expressed as:
=
[ 𝑦𝑖1𝑦𝑖2...𝑦𝑖𝑇]
=
[ 𝑒0...0]
𝛾1 +
[ 0𝑒...0]
𝛾2+. . . . . . . . +
[ 00...𝑒]
𝛾𝑁 +
[ 𝑍1𝑍2...𝑍𝑁]
𝛽 +
[ 휀1휀2...휀𝑁]
− − − − − (4.56)
Where
𝑦𝑖𝑇Χ1 =
[ 𝑦𝑖1𝑦𝑖2...𝑦𝑖𝑇]
, 𝑍𝑖𝑇Χ𝐾 =
[ 𝑧𝑖1,1 𝑧𝑖2,1...
𝑧𝑖𝑇,1
𝑧𝑖1,2𝑧𝑖2,2...
𝑧𝑖𝑇,2
… . . 𝑧𝑖1,𝐾… . . 𝑧𝑖2,𝐾
.
.
.… . . 𝑧𝑖𝑇,𝐾]
, 𝑒/ = (1, 1, … ,1),휀/𝑖1Χ𝑇
= (휀𝑖1,… . . , 휀𝑖𝑇)
𝐸 (휀𝐼) = 0 , 𝐸(휀𝑖휀/𝑖) = 𝛿
2𝑖𝐼𝑇 , 𝐸(휀𝑖휀
/𝑗) = 0 𝑖𝑓 𝑖 ≠ 𝑗
The 𝐼𝑇 is used for TXT identity matrix and parameter estimators 𝛾𝑖 𝑎𝑛𝑑 𝛽 would be
obtained by minimizing
71
𝜆 =∑휀/𝑖
𝑁
𝑖=1
휀𝑖 =∑(𝑦𝑖 − 𝑒𝛾𝑖 − 𝑍𝑖𝛽)/(𝑦𝑖 − 𝑒
𝑁
𝑖=1
𝛾𝑖 − 𝑍𝑖𝛽) − − −− − (4.57)
The partial derivatives of equation (4.57 w.r.t parameter estimators (𝛾 𝑎𝑛𝑑𝛽 and keeping
them equal to zero would yield
𝛾�̂̇� = 𝑦�̅̇� − 𝛽/𝑧�̅̇�
�̂� = {∑∑(𝑧𝑖𝑡
𝑇
𝑡=1
𝑁
𝑖=1
− 𝑧�̅�)(𝑧𝑖𝑡 − 𝑧�̅�)/}−1 {∑∑(𝑧𝑖𝑡
𝑇
𝑡=1
𝑁
𝑖=1
− 𝑧�̅�) (𝑦𝑖𝑡 − �̅�𝑖}
Where
𝑦�̅̇� =∑ 𝑦𝑖𝑡𝑇𝑡=1
𝑇⁄ , 𝑧�̅̇� =
∑ 𝑧𝑖𝑡𝑇𝑡=1
𝑇⁄
The parameter estimator is called Least Square Dummy Variables (LSDV because the
parameter estimator 𝛾�̂̇� requires dummy variables that vary cross-sectionally but constant over
time; whereas 𝛽 does not require the slope dummy variables (Hsiao, 2003; Frees, 2004). Thus,
we could rewrite equation (4.54) as:
𝑌𝑖𝑡 = 𝛾1𝐷1𝑖 + 𝛾2𝐷2𝑖 +⋯+ 𝛾𝑁𝐷𝑁𝑖 + 𝛽 𝑍𝑖𝑡 + 휀𝑖𝑡 −−−−− (4.58)
The first individual of dummy variable is taken value 1 in the sample and zero otherwise.
The dummy variable takes the value 1 for the 2nd individual and zero otherwise, and so on. We
have to test the following hypothesis by using F-(Chow test):
𝐻0 = 𝛾1 = 𝛾2 = ⋯ = 𝛾𝑁
𝐹 − 𝑟𝑎𝑡𝑖𝑜 =(𝑒𝑟𝑟𝑜𝑟 𝑆𝑆)𝑟𝑒𝑑𝑢𝑐𝑒𝑑 − 𝑒𝑟𝑟𝑜𝑟 𝑆𝑆
(𝑛 − 1)𝑆2−−−−− (4.59)
Where error sum of square and S2 are obtained by estimating equation (4.59) with (n-1
degree of freedom. The (𝑒𝑟𝑟𝑜𝑟 𝑆𝑆𝑟𝑒𝑑𝑢𝑐𝑒𝑑) is obtained by estimating reduced model with N-
(n+K) degree of freedom (Frees, 2004).
72
4.4.5.2 Random effect model
The alternative model for estimating Panel data is random effects model by introducing the
intercept for each cross-sectional individual have common intercept 𝛼 and 𝛾𝑖 . The common
intercept 𝛼 is the same for all cross-sectional individuals over time; whereas 𝛾𝑖 exhibits
variation cross-sectional and constant over time. These could be expressed as:
𝛽𝑖 = 𝛼 + 𝛾𝑖 −−−− − (4.60)
And the random effects model would be:
𝑌𝑖𝑡 = 𝛼 + 𝛽/𝑋𝑖𝑡 + 𝜇𝑖𝑡 ; 𝜇𝑖𝑡 = 𝛾𝑖 + 휀𝑖𝑡 −−−−− (4.61)
The error term 𝜇𝑖𝑡 includes idiosyncratic term 휀𝑖𝑡 varies along time and cross-section
individuals and 𝛾𝑖 varies cross-sections but constant over time (Frees, 2004). Most of the
previous literature used total ecological footprint and CO2 emissions as environmental impact
indicator. However, in this study we used the methodology of Frees (2004) to test the effect of
various drivers of total ecological footprint and its components and the relationship is written
as :
ln (𝐸𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡 +
𝛽5𝑙𝑛(𝐹𝐹)𝑖𝑡 + 𝛽6𝑙𝑛(𝐸𝐼)𝑖𝑡 + 𝛽7ln (𝑆𝐼)𝑖𝑡 + 𝛽8ln (𝑀𝐼)𝑖𝑡 + 𝛽9ln (𝐴𝐼)𝑖𝑡 + 𝛽10ln (𝐼𝐸)𝑖𝑡 + 𝜔𝑖𝑡 −
−− (4.62)
ln (𝐶𝑂2𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡 +
𝛽5𝑙𝑛(𝐶𝑂𝐴𝐿)𝑖𝑡 + 𝛽6𝑙𝑛(𝑂𝐼𝐿)𝑖𝑡 + 𝛽7ln (𝐺𝐴𝑆)𝑖𝑡 + 𝛽8ln (𝑀𝐼)𝑖𝑡 + 𝛽9ln (𝐻𝑊)𝑖𝑡 + 휀𝑖𝑡 −−−
−(4.63)
ln (𝐹𝐼𝑆𝐻𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡
+ 𝛽5𝑙𝑛(𝑆𝐹)𝑖𝑡 + 𝜇𝑖𝑡 −−−−− (4.64)
73
ln (𝐶𝑅𝑂𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡
+ 𝛽5𝑙𝑛(𝐴𝐼)𝑖𝑡 + 𝛽6𝑙𝑛(𝐸𝐷𝑈)𝑖𝑡 + 𝛽7ln (𝐶𝐴)𝑖𝑡 + 𝜗𝑖𝑡 −−−−− (4.65)
ln (𝐹𝑂𝑅𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡
+ 𝛽5𝑙𝑛(𝐸𝑃)𝑖𝑡 + 𝛽6𝑙𝑛(𝐸𝐷𝑈)𝑖𝑡 + 𝛽7ln (𝐼𝐸)𝑖𝑡 + 𝜖𝑖𝑡 −−−−− (4.66)
ln (𝐺𝑅𝐴𝑍𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡
+ 𝛽5𝑙𝑛(𝑃𝐿)𝑖𝑡 +𝜓𝑖𝑡 −−−−− (4.67)
𝑙og (𝐵𝑈𝐿𝑇𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1𝑙og (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2𝑙og (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3𝑙og (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡
+ 𝛽5𝑙𝑛(𝑀𝐼)𝑖𝑡 + 𝛽6𝑙𝑛(𝑆𝐼)𝑖𝑡 + 𝛽7𝑙og (𝐸𝑀)𝑖𝑡 + 𝜉𝑖𝑡 −−−−− (4.68)
The data for the ith country is written as:
(
𝑙𝑛𝐺𝐷𝑃𝑖2003,1 ,𝑙𝑛𝐺𝐷𝑃2𝑖2003,2 ……𝑛𝐸𝑀𝑖2003,𝐾,𝑙𝑛𝐸𝐹𝑖2003, … . 𝑙𝑛𝐵𝑈𝐿𝑇𝐹𝑖2003
𝑙𝑛𝐺𝐷𝑃𝑖2005,1 ,𝑙𝑛𝐺𝐷𝑃2𝑖2005,2 ……𝑛𝐸𝑀𝑖2005,𝐾,𝑙𝑛𝐸𝐹𝑖2005, … . 𝑙𝑛𝐵𝑈𝐿𝑇𝐹𝑖2005
. . . . . . . . . . . . . . . . . . . .
𝑙𝑛𝐺𝐷𝑃𝑖2011,1 ,𝑙𝑛𝐺𝐷𝑃2𝑖2011,2 ……𝑛𝐸𝑀𝑖2011,𝐾,𝑙𝑛𝐸𝐹𝑖2011, … . 𝑙𝑛𝐵𝑈𝐿𝑇𝐹𝑖2011)
74
4.4.5.3 The Hausman test
The selection of random and fixed effects approaches is based on the assumption that
whether 𝛾𝑖𝑎𝑛𝑑 𝑋𝑖𝑡 are correlated. To test this assumption (Hausman and Wise, 1978) proposed
test based on the difference between fixed effects and random effects estimates. The significant
value (or larger value of Hausman test goes in favor of FE model. It implies that the estimations
of fixed effect model are consistent when 𝛾𝑖𝑎𝑛𝑑 𝑋𝑖𝑡 are correlated and random effects is
inconsistent.
The procedure of Hausman test is followed by letting 𝛽𝑅�̂� is the vector of estimation of
random effects (excluding the coefficients of time constant variables or aggregate time
variables and 𝛽𝐹�̂� is vector of fixed effects estimations (Frees, 2004). Thus
𝐻 = (𝛽𝐹�̂� − 𝛽𝑅�̂�) [ 𝑣𝑎𝑟(𝛽𝐹�̂�) − 𝑣𝑎𝑟(𝛽𝑅�̂�)]−1(𝛽𝐹�̂� − 𝛽𝑅�̂�) − − − − − (4.69)
The null hypothesis is rejected when the value of H is larger (or statistically significance
and use the fixed effects model; whereas a small value of Hausman test goes in favor of random
effects model.
75
CHAPTER FIVE
TRENDS IN ECOLOGICAL FOOTPRINT,
ECONOMIC GROWTH AND ECOLOGICAL EFFICIENCY
5.1 Introduction
This chapter is organized into four sections. The introduction is being discussed in Section
5.1. In Section 5.2, the trend of ecological footprint and its components, biocapacity, ecological
overshooting, natural resources consumption and socioeconomic factors will be estimated. In
Section 5.3, the trend in ecological footprint, economic growth and ecological efficiency was
discussed. In the section 5.4, the gap between maximum and mean level of ecological
efficiency of total ecological footprint and its components will be estimated.
5.2 Trend of ecological footprint, resources
consumption and socio-economic variables
This section estimate and discuss the trend of ecological footprint and its components,
resource consumption and socioeconomic factors such as per capita GDP, population, level of
urbanization, annual hours worked per employee, export dependency, agriculture,
manufacturing and service intensity. It would also discuss comparison between the total
ecological footprint and its biocapacity and trend of ecological overshooting. Findings of Table
5.1 reveals a decreasing trend of CO2, crop and built-up land footprints from 2005 to 2011,
which implies that area of land required to satisfy humanity demand, urban activities and
assimilation of CO2 emissions are threatened. This is due to rapid increase of energy
consumption while it leads to deteriorating biocapacity of the globe. Findings of this study
shows that forest has larger environmental deterioration than that of grazing and fishing
grounds footprints, in these nations. The decreasing trend of CO2 footprints in period 2005-
76
2011 implies that according to (GFN, 2016a; Li and Zhao, 2016) that demand of CO2 emissions
related resources increased which led to scarce the area of land for assimilation of CO2
emissions - the reported form of CO2 footprint.
Table 5.1
Trend of Ecological Footprint and Its Components
(Global ha/person 2003-2011)
High Income Countries
Year Cropland
Footprint
Grazing
land
Footprint
Forest
Footprint
Fishing
Grounds
Footprint
CO2
Footprint
Built-up
land
Footprint
Total
Ecological
Footprint
2003 1.04 0.23 0.70 0.41 3.86 0.25 6.48
2005 1.15 0.28 0.61 0.17 4.04 0.13 6.40
2007 1.02 0.23 0.70 0.26 3.78 0.11 6.10
2009 1.10 0.40 0.50 0.20 3.10 0.10 5.40
2011 1.20 0.20 0.50 0.20 3.00 0.20 5.30
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org
Figure 5.1 confirms that environmental impact of CO2 is larger than that of other
components of the footprints, due to rapid increases of energy consumption. The share of CO2
footprint reveals that 59 per cent area of land is required for assimilation of CO2 emissions and
wastes generated by these nations, while 37 per cent area of land is required to support the
cropland, forestry, grazing, and fisheries activities. However, only 4 per cent of the area of
land was captured by the built-up footprint during 2003-2011. Yet, the CO2 footprint was the
major environmental degradation’s driver, followed by the cropland and forest footprints.
77
Figure 5.1
Percentage Share of components of Ecological Footprint of High Income Countries
Source: Author’s Computation based on GFN data-set.
Table 5.2 estimate the comparison between total ecological footprint and biocapacity of
high income countries, for the period 2003-2011. The comparison shows that high income
countries have a deficit in resource use because the total ecological footprint is greater than
the biocapacity, during this period. It further implies that these countries have greater demand
for ecological footprint than their biocapacity. Similarly, the ecological overshoot indicates
that these nations do not only consume its entire budget, but also extract its future generation
and other nations’ biocapacity during 2003-2011. However, the findings were consistent with
(GFN, 2014; Jordi et al., 2016), where they argued that demand of high income countries for
planet’s resources and services exceeded than what the earth had regenerated due to higher
standards of living, consumption of resources and goods and services. The question that why
their footprints are more than their biocapacity is further addressed by estimating the trend of
energy consumption (coal, oil and gas and other socioeconomic factors).
16%
11%
4%
6%
4%
59%
Cropland Forest
Grazing land Fishing Grounds
Built-up land CO2 footprint
78
Table 5.2
Total Ecological Footprint vs Biocapacity
High Income Countries
Year Total
Ecological
Footprint
Total Biocapacity Biocapacity
(Deficit or
Surplus)
Ecological
Over Shoot
Global ha/per person
2003 6.48 2.50 (3.98 159%
2005 6.40 3.70 (2.70 73%
2007 6.10 3.10 (3.00 97%
2009 5.30 3.20 (2.10 66%
2011 5.40 3.00 (2.40 80%
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org
The results reported in Table 5.3 reveals that high income nations have almost increasing
trends of energy consumption, except in the year 2009. This was due to lower economic
growth, lower demand for export, agriculture and manufacturing items, and a lower share of
the service sector (Asici and Acar, 2016; GFN, 2016a). The results, further shows that variation
in resource consumption affects both the ecological footprints and biocapacity.
79
Table 5.3
Trend of Resources Consumption, 2003-11
High Income Countries
Year Coal consumption
(thousand million tons)
Oil consumption
(thousand barrels per day)
Gas consumption
(thousand Billion Cubic Feet)
2003 7.7 14.0 15.5
2005 7.8 14.4 16.0
2007 7.9 14.5 16.6
2009 7.2 13.5 16.5
2011 8.1 14.7 18.2
Source: Author’s Calculation based on international energy statistics data set https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=1&pid=1&aid=2
The trend of per capita income, population, urbanization and annual hours-worked per
worker during 2003-2011 of high income countries are reported in Table 5.4. The findings
shows that these countries have almost increasing trend in per capita income, population and
urbanization. However, in the year 2009 per capita income had decreased. This was due to
decreasing trend of work-hours and agriculture intensity, and lower share of export and
manufacturing to the national income. On the other hand, increasing trend of population and
urbanization leads to explain more demand for goods and services that leads to exert pressure
on environment by reducing the biocapacity of these nations during 2003-2011.
80
Table 5.4
Trend of GDP, Population, Urbanization and Hours Works, 2003-11
High Income Countries
Year
GDP Per Capita
(in Million$)
Population
(Million)
Urban population
(Millions of total
population)
Annual hours
worked per
worker
2003 2477 1306 1017 1789
2005 2908 1322 1038 1785
2007 3356 1341 1059 1769
2009 3299 1360 1082 1744
2011 3731 1376 1101 1749
Source: http://www.conference-board.org/data/economydatabase/ and World Bank data set
The export, manufacturing and services as percentage of GDP exhibit increasing trend
during 2003 to 2011. More demand for goods and services lead to increase in consumption of
resources (GFN, 2014, 2016b). In year 2009, shares of these sectors decreased because of
lower demand that reduced consumption of resources and pressure on environment. Findings
of this study, also confirm that they have decreasing trend in agriculture sector because of the
scarcity of cropland footprint and conversion of resources to manufacturing and service
sectors.
Table 5.5
Trend of Export, Agriculture, Manufacturing and Services; 2003-11
High Income Countries
Year
Export of goods & services
(% of GDP)
Agriculture
(% of GDP)
Manufacturing
(% of GDP)
Services
(% of GDP)
2003 24 2 16 72
2005 26 2 17 73
2007 28 1 18 74
2009 25 1 15 73
2011 30 1 19 75
Source: World Development Indicator Dataset, 2016, http://data.worldbank.org/indicator
81
Figure 5.2 explain the share of agriculture, manufacturing and service intensity measured
as percentage to GDP of high income countries. The service intensity accounts to 18 per cent
of GDP followed by manufacturing and agriculture intensity of 17 and 2 per cent, respectively;
which implies that these nations are mainly dependent on service-based activities that demand
for built-up land. The service based activities alongwith scarcity of built-up land would
generate socioeconomic problems that further limit economic growth of high income countries
(Jorgenson and Clark, 2011; Knight et al., 2013). The share of manufacturing in GDP confirms
more demand for resource consumption and generation of CO2 emissions in large volume, and
consequently the area of land required to assimilate CO2 emissions generated by manufacturing
activities becomes scarcer as shown in earlier findings. The share of agriculture implies that,
as percentage of GDP utilization of cropland area, forest, grazing land footprint becomes
lower, because of conversion of resources to service and manufacturing based activities.
Fig. 5.2
Percentage Share of Agriculture, Manufacturing and
Services Intensity of High Income Countries
Source: Author Computation based on WDI dataset, 2016
2%
17%
81%
Agriculture (% of GDP) Manufacturing (% of GDP) Services (% of GDP)
82
Table 5.6 explains the trend of ecological footprints and its components of middle income
countries for the period 2003-2011. The result confirm that environmental impact of crops,
forestry and CO2 emissions is larger than that of other footprints. The argument is supported
by demand for area of land required for prior footprints which is larger than the other footprints.
Similarly, the increasing trend of total ecological footprints also confirms that demand for area
of land required for consumption of goods and services and to assimilate CO2 emissions
generated by these nations have changed by 24 per cent during 2003-2011.
Table 5.6
Trend in Ecological Footprint and Its Components
(Global ha/person 2003-2011)
Middle Income Countries
Year Cropland
Footprint
Grazing
land
Footprint
Forest
Footprint
Fishing
Grounds
Footprint
CO2
Footprint
Built-up
land
Footprint
Total
Ecological
Footprint
2003 0.79 0.17 0.36 0.17 1.19 0.11 3.0
2005 1.06 0.31 0.33 0.11 1.26 0.12 3.2
2007 0.99 0.30 0.44 0.17 1.16 0.14 3.2
2009 1.00 0.30 0.40 0.20 1.20 0.20 3.3
2011 0.98 0.26 0.39 0.13 1.79 0.15 3.7
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org
The share of cropland, forestry and CO2 footprints is almost 84 per cent of total footprints,
confirming that they have a larger environmental impact than the other footprints at the global
level. These nations are in a phase of development, thereby increasing trend of urbanization
further which exerts pressure on consumption of natural resources to meet the demand for
goods and services. In subsequent sections, the ecological footprints and biocapacity of these
countries are compared to assess whether they are in resources surplus or in deficit, during
2003-2011.
83
Fig. 5.3
Percentage Share of Components of Ecological
Footprints of Middle Income Countries
Source: Author Computation based on GFN dataset.
Comparing footprints to biocapacity confirms that they have overshoot in resource
consumption because footprints are more than its biocapacity, which suggests that they
consume the entire budget of resources and some part of its future biocapacity; and therefore,
ecological overshooting of middle income countries during 2005-2011 was 14 per cent of its
biocapacity. It is further estimated that trend of natural resources consumption and the other
socioeconomic factors justify the increasing trend of ecological overshooting.
28%
13%
6%6%4%
43%
Cropland Forest
Grazing land Fishing Grounds
Built-up land CO2 footprint
84
Table 5.7
Total Ecological Footprint vs Biocapacity
Middle Income Countries
Year Total
Ecological
Footprint
Total Biocapacity Biocapacity
(Deficit or
Surplus)
Ecological
Over Shoot
Global ha/per person
2003 2.78 2.84 0.06 -
2005 3.20 3.00 (0.20 07%
2007 3.20 2.90 (0.30 10%
2009 3.30 2.80 (0.50 18%
2011 3.70 3.10 (0.60 19%
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org
The ecological footprint deficit measures resource deficit of a nation in a giving year. More
importantly, such indicators can be associated with sustainable development by governments,
business and NGOs. It would guide them in the direction where they need to go. As the high
and middle income countries accelerate their economic development at the cost of an
increasing trend in ecological footprint deficit as shown by our findings; therefore, they should
try to accelerate their economic development through resource efficiency. It implies that
ecological deficit must be less than biocapacity. In this way sustainable development can be
achieved.
The results reported in Table 5.8 shows that they have increased trend of natural resource
consumption. During 2003-2011, the coal consumption increased by 67 per cent, while oil and
gas consumption increased by 33 per cent and 19 per cent, respectively. The findings confirm
that demand of the area of land required for assimilation of CO2 emissions has increased by 51
per cent and biocapacity deficit reached to 0.4 gha/person, which leads to explain the
environmental pressure through ecological overshooting by 14 per cent.
85
Table 5.8
Trend of Resources Consumption, 2003-11
Middle Income Countries
Year Coal consumption (thousand million tons)
Oil consumption
(thousand barrels per day)
Gas consumption
(thousand Billion Cubic Feet)
2003 4.6 3.6 6.2
2005 5.2 4.0 6.7
2007 5.9 4.2 7.1
2009 6.6 4.4 7.1
2011 7.7 4.8 7.4
Source: Author’s Calculation based on international energy statistics data set https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=1&pid=1&aid=2
In Table 5.9 the results confirm that per capita income, population and urban population
have an increasing trend. Such increasing trend leads to demand for goods and services for
consumption of natural resources and to built-up land for support of urbanization (A. M.
Usama et al., 2014; Al-Mulali et al., 2015; GFN, 2016a). The growth rates of population and
urbanization are respectively 5 percent and 18 percent, where the cropland, forest, fisheries,
CO2, grazing and built-up land footprints increased by 29 percent during 2003-2011.
86
Table 5.9
Trend of GDP, Population, Urbanization and Hours Works; 2003-11
Middle Income Countries
Year
GDP Per Capita
(in Million$
Population
(Million Urban population
(Millions of total population
Annual hours
worked per
worker
2003 1241 4586 547 2019
2005 1665 4705 571 2031
2007 2353 4821 596 2024
2009 2725 4938 623 2003
2011 3792 5058 648 2001
Source: http://www.conference-board.org/data/economydatabase/ and World Bank data set
The reported result of Table 5.10 reveals that export, manufacturing and service sectors are
major contributors to the GDP of middle income countries. The trend analysis confirm that
share of these sectors increased, while agriculture’s contribution to GDP have decreased. The
change in export, manufacturing and services are 21, 25 and 12 per cent respectively, which
implies that percentage of GDP increased the manufacturing and services; while agriculture
decreased as percentage of GDP. It further implies that agriculture intensive economies
converted to manufacturing and service intensive economies.
87
Table 5.10
Trend of Export, Agriculture, Manufacturing and Services; 2003-11
Middle Income Countries
Year
Export of goods & services
(% of GDP)
Agriculture
(% of GDP)
Manufacturing
(% of GDP)
Services
(% of GDP)
2003 28 13 24 51
2005 31 11 25 52
2007 32 11 27 54
2009 32 10 28 56
2011 34 10 30 57
Source: World Development Indicator Dataset
88
5.3 Trend of ecological footprints, economic growth and
ecological efficiency
This section explains the trend of ecological footprints, economic growth and ecological
efficiency of high and middle income countries for the period of 2003-2011. The ecological
efficiency is constructed to explain as to how much the economic output is yielded by utilizing
the per global hectare area in the form of gross domestic product. Results of Table 5.11
indicate the increasing trend of ecological efficiency and economic growth from 2003-2011
(except for the year 2009) where economic growth was negative. This is due to less utilization
of area of land, resources consumption and lower demand for agriculture, manufacturing and
export of goods and services. In year 2011 economic growth is positive which leads to higher
demand for ecological footprints and resources consumption. The increasing trend of
ecological efficiency and decreasing trend of total ecological footprints confirms that economic
output in terms of gross domestic product is yielded in efficient way because of less utilization
of per global hectare of area.
Table 5.11
Ecological Footprint, Economic Growth and Ecological Efficiency; 2003-2011
Year
High Income Countries
Total Ecological Footprint
(gha/per capita)
Economic Growth
(annual %)
Ecological Efficiency
(1000 of income/gha)
2003 6.48 2.21 1.91
2005 6.40 2.86 2.27
2007 6.10 2.88 2.75
2009 5.30 -3.47 2.11
2011 5.40 1.91 2.66
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Data set
89
The estimated values of ecological footprints are presented in Table 5.12 which shows an
increasing trend during 2003-2011; while economic growth and ecological efficiency
increased in the period of 2003-2007. In year 2009, the reduction of economic growth led to
reduce the ecological efficiency; and because of the increasing economic growth, the
ecological efficiency improved in the year 2011. From the prior results, nations with more per
capita income appear to have larger ecological footprints and efficiency.
Table 5.12
Ecological Footprint, Economic Growth and Ecological Efficiency; 2003-2011
Year
Middle Income Countries
Total Ecological Footprint
(gha/per capita)
Economic Growth
(annual %)
Ecological Efficiency
(1000 of income/gha)
2003 2.78 2.8 0.45
2005 3.20 3.5 0.52
2007 3.20 4.2 0.74
2009 3.30 1.7 0.72
2011 3.70 3.0 1.02
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Data set
90
5.4 Analysis of ecological efficiency index, maximum and
mean level of ecological efficiency
In this section the ecological efficiency index of total footprints and its components using
chain-based approach proposed by Qiu (2013) is estimated. The value of index is greater than
one which confirms that efficiency of resource consumption in production of economic output
improved in the current year than the previous year. The findings presented in Figure 5.4
suggest that ecological efficiency of both the high and middle income countries in the current
year has improved than the previous year. However, in year 2009 they had less ecological
efficiency than the year 2007 which was due to the decreasing economic output. The result
also suggest that ecological efficiency index of high income countries is less than the middle
income countries; while it was found that these countries consumed resources and services at
a faster rate. The ecological efficiency or resource intensity of each country in high and middle
income regions is relative to the best performer (i.e., highest ecological efficiency and the mean
of resource intensity), presented in Appendix-D of the study. The table 2D shows that the
environmental impact intensity of Pakistan in year 2003 was 1.124 gha/GDP and reached to
1.154 gha/GDP in year 2005. This increasing trend is due to; along with expansion of economic
development, the amount of material resources has been increased in Pakistan. However, on
ward the Pakistan’s environmental intensity has been declined and various reasons are
associated with this trend. Firstly, the financial crises leads to mitigate demand of material
resource and increased supply of biocapacity of Pakistan. Secondly, as the share of service
sector in GDP has been increased and reduced CO2 footprint. Thirdly, the forest and grazing
land has accelerated in Pakistan from 4.8% in year 2005 to 5.17% in year 2011 due to
implementation of environmental protection policies. Fourthly, the numbers of power plants
from use of coal have been converted to natural gas. As a result, the CO2 emissions reduced
and consequently increased supply of biocapacity of Pakistan. The implementation of Micro
91
Hydro Power (MHP) plants instead of wood consumption and CNG instead of coal usage for
domestic purposes also reduced forest and CO2 footprint of Pakistan.
The study also evaluated relative ecological efficiency or relative resource intensity8 of each
high income countries as depicted in Table 3D for the period of 2003-2011. The relative
resource intensity for each nation is calculated by dividing ecological efficiency or
environmental impact intensity per unit of gross domestic product (i.e., EF/GDP) on mean
(0.191 gha/GDP) and the best performer in ecological efficiency (environmental impact
intensity=0.077gha/GDP) of the sample countries. Then the rank is assigned to sample
countries based on these two methods where rank one represents the lowest resource intensity
(most eco-efficient country). According to the findings, the rank of Switzerland is one, which
represents that she is the best performer in ecological efficiency. The rank of Estonia among
High income countries is 77, which represents that it is the lowest performer in ecological
efficiency because the environmental impact intensity of per unit of gross domestic product of
Estonia is the largest. The most efficient nation is Switzerland with one-fourth (0.40) of the
cross-sectional mean environmental impact intensity per unit of gross domestic product; while
the least efficient nation is Estonia with more than double (2.55) mean environmental impact
intensity per unit of gross domestic product. It implies that 6.37 fold (2.55/0.40) difference in
ecological efficiency between Switzerland (the most eco-efficient) and Estonia (the least
efficient).
There are various factor behind best ecological efficiency performance of Switzerland.
Firstly, improvement in public transport instead of private transport leads to mitigate
Switzerland CO2 footprint. Secondly, improve share of service sectors (i.e. 71% in 2010) and
less dependency on resource intensity sectors increased Switzerland’s ecological biocapacity.
8 resource intensity is inverse of ecological efficiency. Higher resource intensity implies lower ecological efficiency.
92
Thirdly, Switzerland with collaboration of business sector to have implanting environmental
friendly technology and through reducing agriculture and forest footprint with various
agreement for the protection of local environment particularly improve his ecological
efficiency. As the National Action Plan monitor ecological condition and identify the
deficiency on regular basis. Fourthly, improvement in water footprint through implementation
of water waste treatment plants has been improved its ecological efficiency.
There are various factors behind lowest ecological efficiency performance of Estonia. Firstly,
huge dependency on oil shale based energy production which has been increased Estonia’s
CO2 footprint by 80%. Secondly, inefficient use of natural resources to make material goods
and services. Thirdly, the average age of forest is lessening and lessening because of increasing
trend in built-up land footprint.
Similarly, it is also calculated that ecological efficiency of each middle income countries
is based on mean and the best performer in resource intensity for the period of 2003-2011. The
most efficient nation, according to these two methods is Timor-Leste and the least ecological
efficiency or the largest environmental impact intensity per unit of gross domestic product’s
country is Congo, whose rank is 77. The most efficient nation, Timor-Leste has 5/6 (0.12)
cross-sectional mean environmental impact intensity of per unit of gross domestic product,
while the least efficient nation, Congo has more than threefold (3.53) mean environmental
impact intensity per unit of gross domestic product. It implies 29.41 fold (3.53/0.12) difference
in ecological efficiency between Timor-Leste (the most eco-efficient) and Congo (the least
efficient).
The Timor-Lest has achieved improvement in agriculture ad forest biocapacity with
cooperation of NGOs and rural communities. The country also initiated sustainable energy
power projects for example wind power, solar power and natural gas instead of coal
consumption to meet the power demand with less environmental impact possible.
93
Improvement in grazing land, LPG instead of firewood improve forest biocapacity and thus
ecological efficiency of Timor-Lest.
The lowest ecological efficiency of Congo can be associated with corruption, war and
political instability leads to reduce economic expansion in the sample period. While on other
side, huge exploitation of natural resources has been sluggish Congo’s ecological performance.
It is concluded from the above discussion that there is a greater variability between middle
and high income countries’ ecological efficiency (29.41 fold and 6.375 fold) differences
between the most and least ecological efficiency nations, respectively. Therefore, improvement
in ecological efficiency in total ecological footprint, cropland, grazing land, forest, fishing
grounds, CO2 footprint and built-up land footprint in the sample countries could lead to reduce
the difference between the most and least eco-efficient nations. Thus, we also find the gap
between mean and the maximum level of ecological efficiency which mark the potential in
total ecological footprint and its components.
94
Fig. 5.4
Ecological Efficiency Index of High and Middle Income Countries: 2005-11
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org
In Table 5.13, the estimated gap between the maximum and mean level of ecological
efficiency of high income countries through the computation process is proposed by Qiu
(2013), where 𝑅𝐼𝑖𝑡 is used to reflect the gap between a country’s i efficiency in resources
utilization and maximum ecological efficiency of year t among a group of nations. The value
of RI revealed that fishing grounds footprints has lower difference from its best performer,
followed by built-up land footprint, CO2 and other components of total ecological footprint. It
implies that the cropland, grazing land and forest footprints have more room in order to achieve
its best performer (maximum level), followed by CO2, built-up land and fishing grounds
footprint respectively.
Table 5.13 The Gap between Efficiency in Resources Utilization
and Maximum Level of Ecological Efficiency, 2003-11
High Income Countries
Resources
(millionUS$/gha)
Total
Ecological
footprints
Cropland
footprint
Grazing
land
footprint
Forestland
footprint
CO2
footprint
Finishing
ground
land
footprint
Built-up
land
footprint
Max EE 21085 2785 636542 98431 14576 174538 177071
Mean EE 8540 551 103384 24904 1828 47878 41722
𝑅𝐼𝑖𝑡 2.64 15.16 7.97 3.65 4.24
Figure 5.5 focus on more room on components of ecological footprint for achieving its
maximum level of ecological efficiency in percentage. The cropland, grazing land and forest
95
footprints have 49 per cent more room to achieve its maximum level of efficiency, while CO2
has 25 per cent, followed by 13 per cent and 12 per cent built-up land. The fishing ground have
more potential to achieve its maximum level of efficiency in near future, by high income
countries.
Fig. 5.5
Percentage share of components of Total
Ecological Footprints in High Income Countries
Source: Author Computation based on GFN Dataset.
In Table 5.14, there is an estimated gap between the mean and maximum levels of
ecological efficiency at the disaggregate level of middle income countries, for the period of
2003-2011. The value of RI reveal that CO2 footprint has lower difference from its best
performer, followed by built-up land footprint, fishing grounds and other components of the
total ecological footprint. It implies that other components like cropland, grazing land and
forest footprint have more room in order to achieve its best performer (maximum level of
ecological efficiency), followed by fishing grounds, built-up land, fishing ground footprint and
CO2 footprint, respectively.
42%
19%
21%
18%
Cropland,Grazing & Forest footprints Built-up land
Fishing Grounds footprint CO2 footprint
96
Table 5.14
The Gap between Efficiency in Resource Utilization
and Maximum level of Ecological efficiency, 2003-11
Middle Income Countries
Resources
(millionUS$/gha)
Total
Ecological
footprints
Cropland
footprint
Grazing
land
footprint
Forest
land
footprint
CO2
footprint
Finishing
ground
land
footprint
Built-up land
footprint
Max level
Ecological
Efficiency
562 2987 64233 98431 7775 322473 182969
Mean level
Ecological
efficiency
256 1183 22595 24904 2054 73121 44325
𝑅𝐼𝑖𝑡 2.19 9.32 3.78 4.41 4.12
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Data set
The results of the Figure 5.6 shows that in order to achieve maximum efficiency of crop,
forest and grazing land, footprints have 42 per cent more room to promote its efficiency
followed by fisheries, built-up land and CO2 of 21 per cent, 19 percent and 18 per cent,
respectively, in case of middle income countries. Such discrepancy leads to estimate inequality
and environmental intensity of resources of high and middle income countries.
97
Figure 5.6
Percentage Share of Components of Total Ecological
Footprint in Middle Income Countries
Source: Author Computation based on GFN Dataset.
42%
19%
21%
18%
Cropland,Grazing & Forest footprints Built-up land
Fishing Grounds footprint CO2 footprint
98
CHAPTER SIX
ECOLOGICAL FOOTPRINT, ENVIRONMENTAL
IMPACT INTENSITY AND INCOME INEQUALITY
6.1 Introduction
This chapter is classified into four Sections. Section 6.1 covers the introduction of the
chapter. In Section 6.2 and 6.3, the estimated inequality and per capita mean values of
ecological footprint, income and environmental impact intensity of high and middle income
countries are presented, respectively. Section 6.4 focus on discussion regarding inequality
between High and Middle income countries.
6.2 Ecological footprint, environmental impact intensity
and income inequality of high income countries
In the previous chapter, the estimated trend of ecological footprint, biocapacity, ecological
overshooting, consumption of natural resources and other socioeconomic factors where the
findings reveal that ecological footprint demand and resource consumption and services are
much faster in high income countries, than the middle income countries that lead to generate
environmental degradation at global level. It is supported by Asici and Acar (2016); GFN
(2016a).
In this chapter, the environmental impact intensity, per capita ecological footprint and
income inequality is estimated through the Atkinson Index. The results reported in Table 6.1
reveal that the share of Atkinson index of equality of environment intensity is 31 per cent less
than the equality in per capita income of 45 per cent which leads to produce only 25 per cent
of equality in share of land among high income nations9 while the average total ecological
9 The percentage share from Table 6.1 to Table 6.14 of total ecological footprint, its component, per capita
income and environmental impact intensity obtained divided each Atkinson value on its total share.
99
footprint is 5.03 gha/person and environmental impact intensity is 2.01 footprints per $1000
of income. It implies that there is more variation in resources consumption and demand for
goods and services that leads pressure on the environment. The trend in the environmental
impact intensity of each country for high income countries is presented in Appendix-D.
According to the trend in environmental impact intensity of high income countries depicted in
Table 1D, shows that 57 percent of the sample countries follow mixed trend in their
environmental intensity in 43 percent countries. Similarly, the trend in environmental intensity
of middle income countries is depicted in Table 2D which shows that 60 percent of the sample
countries follow mixed trend in environment intensity and 40 percent middle income countries
follow a declining trend. Thus, suggesting that countries having a mixed trend in their
environmental intensity should follow the policy of the sample countries where their
environmental impact intensity of per unit of gross domestic product declined during the
sample period.
Table 6.1
Atkinson Index of Equality: Total Footprint, per Capita Income, and Environmental
Intensity, 2003-11
Total Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.340
5.03
0.650
$3154
0.450
2.01
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income
100
Table 6.2 shows the mean of cropland footprint, per capita income and environmental
intensity and the Atkinson indices of equality. The estimated values of Atkinson index of
environmental intensity and cropland footprint reveal that they have larger inequality than the
per capita income (because of 1-Ay is close to one in the period of 2003-2011). The share of
Atkinson index of equality of environmental intensity is 30 per cent, followed by cropland
footprint and per capita income of 31 per cent and 39 per cent. It implies that there is a large
variation in the area of land demanded for cropland among high income countries. According
to mean, the area of land required to support cropland footprint is 0.91gha per capita and
environmental intensity is 0.28 footprints per $1000 of income.
Table 6.2
Atkinson Index of Equality: Cropland Footprint, per Capita Income, and Environmental
Intensity, 2003-11
Cropland Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.514
0.910
0.650
$3154
0.510
0.280
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author’s calculation, based on F /y (Total Footprint per unit of income.
Table 6.3, estimate the Atkinson indices of environment intensity, at per capita income and
the grazing land footprint, alongwith its mean values. According to the Atkinson index, the
101
distribution of per capita income and grazing land footprint exhibit more inequality than the
environmental impact intensity (because 1-Aw is close to one). The environmental impact per
unit of economic output is 40 per cent and distribution of land to support grazing activities is
28 per cent. The Atkinson index of equality of per capita income is 32 per cent less than that
of environmental impact intensity and grazing land footprint exhibits lower environmental
intensity per unit of economic output. The mean grazing land footprint is 0.27 gha per capita
and the environmental impact intensity is 0.086 footprints of per $1000 of income.
Table 6.3
Atkinson Index of Equality: Grazing Footprint, per Capita Income, and
Environmental Intensity: 2003-11
Grazing land Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.582
0.270
0.650
$3154
0.830
0.086
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
102
Table 6.4 estimate the mean and Atkinson index of environmental intensity of per capita
income and forest footprint. According to the Atkinson index, the distribution of per capita
income and the forest footprint exhibit more inequality than the environmental intensity. The
share of Atkinson index of equality with respect to environmental intensity is 38 per cent
greater than the equality in per capita income and the forest footprint whose equality shares are
32 per cent and 30 per cent, respectively. According to the mean, the area of land required to
support forest activities demanded by these nations was 0.58 gha per capita and its environment
intensity was 0.27 footprints per $1000 of income. These results suggest that high income
countries have large inequality in distribution of forest land footprint and the environmental
intensity.
Table 6.4
Atkinson Index of Equality: Forest Footprint, Per Capita Income, and Environmental
Intensity, 2003-11
Forest Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw
Mean, µw
(footprint per $1000 income)
0.595
0.580
0.650
$3154
0.752
0.270
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
103
Table 6.5 estimate the Atkinson index and the mean values per capita CO2 footprint and
income and environment intensity. The calculation of the Atkinson index shows the CO2
footprint inequality related to environmental impact intensity and the per capita income.
According to the index, the distribution of per capita Co2 footprint exhibited more inequality
than environmental intensity and the per capita income. It implies that variation in resource
consumption, annual hours worked per employee, manufacturing and service intensities; and
the CO2 footprint are responsible factors for inequality in environmental intensity; as suggested
by the results. During the period 2003-2011 and according to the index, the distribution of
environmental intensity and CO2 footprints exhibit 27 per cent and 19 per cent equality and
the per capita income of equality is 54 per cent. The average environmental impact of CO2 is
0.95 footprints per $1000 of income, and its footprint is 2.98 gha per capita. It implies that
CO2 has a larger environmental impact intensity that requires more land to assimilate CO2
emissions of high income countries.
Table 6.5
Atkinson Index of Equality: CO2 Footprint, per Capita Income,
and Environmental Intensity, 2003-11
CO2 Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.230
2.98
0.650
$3154
0.320
0.950
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income
104
Table 6.6 reports the Atkinson index of income, environmental intensity and fishing
grounds footprints, and its average values. The average in environmental intensity shows that
impact per $1000 of income on environment is 0.05 footprints and the rea of land required to
support fishing activities is 0.17 gha per capita. According to the Atkinson index of equality,
distribution of fishing grounds footprints exhibits more inequality than income and
environment intensity (1-Ay close to zero). The share of environmental intensity and income
is 38 per cent, and 37 per cent is larger than that of fishing grounds footprints of 25 percent.
Table 6.6
Atkinson Index of Equality: Fish Footprint, per Capita Income, and Environmental
Intensity, 2003-11
Fisheries Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.445
0.170
0.650
$3154
0.672
0.054
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
The reported results of Table 6.7 include Atkinson indices and average values of income,
environmental intensity and built-up footprints. The average built-up footprint is 0.14 gha per
capita and the environmental intensity is 0.04 footprints per $1000 of income. Calculation of
the Atkinson index of built-up land footprints exhibits more inequality than income and
environmental intensity (1-AF is close to zero). The shares of per capita income and intensity
105
is 46 per cent, and 33 per cent and is larger than that of the built-up land footprints of 25
percent.
106
Table 6.7
Atkinson Index of Equality: Built-up Footprint,
per Capita Income, and Environmental Intensity, 2003-11
Built-up land Footprinta Per Capita Income Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.308
0.140
0.650
$3154
0.465
0.044
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
6.3 Ecological footprint, environmental impact intensity and
income inequality of middle income countries
In the following sections, the Atkinson indices and mean footprints, income and the
environmental intensity of middle income countries are estimated. The mean environmental
intensity is 0.94 footprints per $1000 income and the area of land required to support human
activities is 2.23 gha/person. According to the Atkinson index of equality, the distribution of
per capita income and ecological footprints exhibits more inequality than environmental
intensity. The share of Atkinson index of environmental intensity is 50 per cent larger than the
ecological footprint and the income share of 26 per cent and 24 per cent , respectively . The
trend in the impact intensity of each country (for middle income countries) is presented in
Appendix-D of the study.
107
Table 6.8
Atkinson Index of Equality: Total Footprint, per Capita income,
and Environmental Intensity, 2003-11
Total Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.310
2.23
0.346
$2355
0.650
0.942
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
Table 6.9 shows the estimated means of cropland footprint, per capita income,
environmental intensity and its Atkinson indices. The mean environmental impact intensity is
0.43 footprints of per $1000 of income and area of land required for cropland footprint are
0.98gha per capita. According to Atkinson index, distribution of per capita income and
cropland land footprint exhibits more inequality than environmental intensity. The distribution
share of environmental intensity, per capita income and cropland land footprints of equality
are respectively 50 percent, 22 percent and 28 percent.
108
Table 6.9
Atkinson Index of Equality: Cropland Footprint, per Capita Income,
and Environmental Intensity, 2003-11
Cropland Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.450
0.980
0.346
$2355
0.790
0.425
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
Table 6.10 shows the estimated means of grazing footprints, per capita income,
environmental intensity and its Atkinson indices. The mean environmental impact intensity is
0.12 footprints/$1000 of income and area of land required for grazing footprint which is 0.28
gha per capita. The estimated value of per capita income and grazing footprint confirms more
inequality than the environmental intensity. The share of grazing footprint, per capita income
and environmental intensity is 29 per cent, 30 percent and 41 per cent of equality.
109
Table 6.10
Atkinson Index of Equality: Grazing Footprint,
per Capita income, and Environmental Intensity, 2003-11
Grazing land Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.351
0.281
0.346
$2355
0.490
0.120
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
The estimated mean of environmental intensity and forest footprint reported in Table 6.11
are 0.18 footprints/$1000 of income and 0.35 gha/person. According to the Atkinson index
distribution of income, forest and environmental intensity exhibit more inequality. Forest has
30 per cent of equality distribution followed by income and intensity of 30 per cent and 37 per
cent of equality.
110
Table 6.11
Atkinson Index of Equality: Forest Footprint, per Capita Income,
and Environmental Intensity, 2003-11
Forest Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.310
0.350
0.346
$2355
0.380
0.150
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
Table 6.12 reported indices and means of CO2, income and environmental intensity of CO2
footprints. With reference to the mean value, CO2 environmental intensity is 0.37
footprints/$1000 of income and its footprint is 0.87 gha per capita. The estimated value of
Atkinson indices exhibited the inequality in environmental intensity and CO2 footprint is
relatively larger than inequality in per capita income.
111
Table 6.12
Atkinson Index of Equality: CO2 Footprint, per Capita Income,
and Environmental Intensity, 2003-11
CO2 Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.350
0.874
0.346
$2355
0.310
0.371
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
The average estimated value of environmental intensity and forest footprint reported in
Table 6.13 are 0.18 footprints/thousands of income and 0.35gha/person, respectively. The
Atkinson index with reference to environmental intensity and fisheries exhibits 62 per cent and
80 per cent inequality in environmental intensity and fisheries footprints among middle income
countries exist.
112
Table 6.13
Atkinson Index of Equality: Fish Footprint, per Capita Income,
and Environmental Intensity, 2003-11
Fisheries Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.204
0.090
0.346
$2355
0.381
0.038
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
The Atkinson index of built-up land footprint reported in Table 6.14 exhibits that inequality
in per capita income and built-up land footprint is relatively higher than inequality in
environmental intensity. The relatively equal distribution of environmental intensity and per
capita income could lead to minimizing built-up land footprint inequality among the middle
income countries. The average environmental intensity and built-up footprint are 0.021
footprints/thousands of income and 0.065 gha per capita, respectively.
113
Table 6.14
Atkinson Index of Equality: Built-up Footprint, Per Capita Income,
and Environmental Intensity, 2003-11
Built-up land Footprinta Per Capita Incomeb Environmental Intensitya,b,c
(1-AF)
Mean, µF
(global ha/person)
(1-Ay)
Mean, µy
(US dollars)
(1-Aw)
Mean, µw
(footprint per $1000 income)
0.281
0.065
0.346
$2355
0.451
0.028
a Source: Global Footprint Network (2015, www.footprint network.org.
b Real GDP per capita. Source: World Bank Data
c Author's calculation, based on F /y (Total Footprint per unit of income.
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6.4 Discussion
From the prior discussion, it is concluded that nations with high income have higher
measures of environmental intensity and therefore have larger ecological footprints. The per
capita income of high and middle income countries are $3154 and $2355, respectively, and
therefore, there is a larger measure of footprints and environmental intensity by high income
countries in the case of CO2, forest, fisheries and built-up land footprint in the period of 2003-
2011. It shows that the demand for area of land required for CO2 footprint, forest, fishing
grounds and built-up land footprint in case of high income countries is greater than the middle
income countries, which lead to greater environmental impact intensity and ecological over
shooting, as described in Chapter Five of this study.
The inequality of environmental intensity is larger than per capita income of high income
countries and therefore relatively, equal distribution of intensity can be contributed to the
distribution of ecological footprints which is more equal than the distribution of income. In
case of middle income countries, relatively equal distribution of income could contribute to
the distribution of footprints that is more equal than the distribution of environmental impact
intensity.
Similarly, inequality in per capita income, total ecological footprint, cropland footprint,
grazing land, forest, fishing grounds and built-up land footprint have comparison between the
high and middle income countries. reveals that there should be more equal distribution in these
footprints for middle income countries which will lead to reduce its environmental impact
intensity, while there should be more equal distribution in CO2 footprints for high income
countries. This will lead to reduce its environmental impact intensity because the CO2 footprint
has larger inequality for high income countries.
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CHAPTER SEVEN
THE DRIVING FORCES OF TOTAL ECOLOGICAL
FOOTPRINT AND ITS COMPONENTS
7.1 Introduction
This chapter is classified into five Sections. Section 7.1 explore the introduction of the
chapter. Sections 7.2, 7.3 and 7.4 estimate the various driving forces of the total ecological
footprints and its components of high and middle income countries. Section 7.4 covers the
discussion and the findings.
7.2 The driving forces of total ecological footprint and
its component in high-middle income countries
This section estimates and interprets the various driving forces’ impact on total ecological
footprint, cropland footprint, forest, fishing grounds, grazing land, CO2 footprint and built-up
land footprint by using combined panel of high-middle income countries. Table 7.1 presents
the effect of driving forces on total ecological footprint . The findings confirm the existence of
the EKC hypothesis between affluence and total ecological footprint. It implies that as
economic development increases, ecological footprint increases while further level of
economic development decreases the ecological footprint of sample countries. Besides,
population, the level of urbanization, fossil fuel, manufacturing and agricultural intensity
increases the total ecological footprint. The impact of population on ecological footprint is
larger than the other driving forces, followed by the manufacturing and level of urbanization.
The coefficients associated with these driving forces are positive and statistically significant.
The response of one percent increase in population, agriculture intensity and the level of
urbanization on ecological footprint is 0.64 per cent, 0.58 per cent and 0.13 per cent,
respectively. The impact of export intensity and ecological efficiency is negative and
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statistically significant. It shows that the response of one per cent increase in these factors on
ecological footprint are 0.36 per cent and 2.56 per cent. Thus, the improvement in ecological
efficiency is decoupling the environmental degradation. The findings suggest that proper urban
planning, environmental friendly service, manufacturing and agricultural activities, as well as
low carbon intensity energy use in the sample countries should mitigate the tension between
environmental degradation and sustainable development.
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Table 7.1
The Driving Forces of Total Ecological Footprint:
High-Middle Income Countries ( Random Effect Model)
Independent Variables Coefficients t-Statistic
ln(GDP) 2.55* 8.22
ln(GDP2) -0.11* -14.23
ln(POP) 0.64* 9.62
ln(UR) 0.13* 3.04
ln(FF) 0.06* 2.42
ln(EI) -0.36* -5.16
ln(SI) 0.31* 3.94
ln(MI) 0.58* 12.18
ln(AI) 0.04** 2.09
ln(IE) -0.02 -0.55
Ecological Efficiency -2.56* -3.10
Constant -5.04* -8.75
R-Squared: 0.89
F-Statistic: 666.33* Huasman test : 0.0000 (0.921)☼
Sample: 2003Q1-2011q4; & Cross-sectional units = 95; periods included=36
Total Panel (Balanced observations) =3420
*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5% supports the RE model
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
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Table 7.2 focus on the effect of various driving forces on cropland footprint, fishing
grounds and forest footprint by using the aggregated dataset of high-middle income countries.
With reference to determinants of cropland footprint, the empirical results support the EKC
hypothesis. Initial level of economic development increases the cropland footprint, and further
level of economic development increases the sample countries’ cropland footprint. The other
driving forces that contribute to increase in the cropland footprint are population, agricultural
intensity and consumption of agriculture items. The coefficients associated with these forces
are positive and statistically significant. It implies that a one per cent increase in population,
agricultural intensity and consumption of agriculture items increase the cropland footprint by
0.77 per cent, 0.25 per cent and 0.04 per cent, respectively. However, the major driving forces
that greatly contribute to increase the cropland footprint are the initial level of economic
development, followed by population and agricultural intensity. The impact of ecological
efficiency is negative and statistically significant. It implies that improvement in ecological
efficiency decreases the cropland footprint. In the light of the findings, it is suggested that
continued process of economic development and efficiency in material resource consumption
reduces the environmental degradation. The findings are consistent with Wiedmann et al.
(2015); Asici and Acar (2016) results of driving forces of crops footprint that increase further
in level of economic development which leads to increased cropland footprints. It also
provided strong support for the hypothesis that footprint increases with initial level of
affluence.
The findings of fisheries footprint confirms the EKC relationship which reveals that initial
stage of economic development leads to increase fishing ground footprints while the economic
development of sample countries leads to reduce their fishing ground footprint, further. The
results indicate a positive relation between fisheries footprints and the consumption of fisheries
products, and a positive and insignificant impact of population and urbanization on fisheries
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footprints. It implies that a one per cent increase in consumption of fisheries products will
increase the fishery footprints by 0.63 per cent. However, the impact of ecological efficiency
on fishery footprints is negative and statistically significant. It shows that improvement in
ecological efficiency decreases the fishery footprints. Therefore, growth in income, mitigation
of the overexploitation of fish and fishery products, and further improvement in ecological
efficiency would reduce the fishery footprints.
Findings of forest footprints confirm the validity of EKC hypothesis, because the effect of
initial level of affluence on forest footprint is positive as it further increase the level of
affluence which reduce the forest footprints. The other driving forces that accelerate the forest
footprint are population, education and export of primary products. It implies that a one per
cent increase in these driving forces will increase the forest footprints by 0.72 per cent, 0.11
per cent and 0.05 per cent, respectively. The findings also indicate a negative and significant
relation among urbanization, ecological efficiency and the forest footprints. The improvement
in ecological efficiency reduces the forest footprint while the level of urbanization reduces the
environmental sustainability, further. The findings suggest that investment in the education
sector, proper planning for urbanization, replacing export of forest substitute items, and further
increase the ecological efficiency which can lead to reduce the forest footprint.
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Table 7.2
The Driving Forces of the Components of Ecological Footprint:
High-Middle Income Countries (Random and Fixed Effect Models)
Cropland footprint Fisheries footprint Forest footprint
Independent Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic
ln(GDP) 2.46* 2.65 0.84* 3.74 2.67* 13.26
ln(GDP2) -0.01* -2.26 -0.04* -4.89 -0.03* -11.48
ln(POP) 0.77* 8.29 0.07 0.44 0.72* 7.31
ln(UR) 0.12 0.30 0.05 0.22 -0.16** -1.80
ln(AI) 0.25* 2.25 - - - -
ln(EDU) 0.10 0.65 - - 0.11* 2.42
ln(CA) 0.04** 1.67 - - - -
ln(SF) - - 0.63* 11.50 - -
ln(EP) - - - - 0.05** 1.84
Ecological Efficiency -1.08* -2.70 -1.23* -2.28 -5.87* -14.53
Constant -7.74* -2.74 2.70** 1.92 4.87* 14.04
R-Squared: 0.85
F-Statistic: 481.23* Huasman test: 0.000 (0.962) ☼
0.69 192.53*
88.71(0.000) ●
0.75 247.60*
09.40(0.210) ☼
Sample: 2003Q1-2011q4; & Cross-sectional units = 95; periods included=36
Total Panel (Balanced observations) =3420
*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less
than 5% supports the FE model.
Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
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Table 7.3 reports the impact of various driving forces of CO2 footprint, grazing land and
built-up land footprints. With reference to the empirical findings of CO2 footprint, the findings
support the hypothesis; because, increase at initial level of economic development increases
the CO2 footprint and the level of economic development, decreases the CO2 footprint further.
The other driving forces that contribute to increase the footprint are population, urbanization,
coal, oil, gas and the manufacturing intensity. However, the major contributor to CO2 footprint
is population, followed by urbanization and manufacturing intensity. It implies that a one per
cent increase in these factors increases the footprint of CO2 emissions by 0.66 per cent, 0.42
per cent and 0.15 per cent, respectively. The possible recommendation in the light of findings
of CO2 footprint is that sample countries should promote the implication of solar and wind
power generation. Thus, investment in renewable resources would reduce the CO2 footprint
that mostly occurred from the fossil fuel consumption (Uddin et al., 2017).
With reference to the grazing land footprint, the findings support the EKC hypothesis. The
initial level of economic development increases the grazing land footprints and the level of
economic development decreases the footprint, further. However, the impact of initial level of
economic development on grazing land footprint is larger than the further level of economic
development. It implies that with one per cent increase in the initial economic development;
grazing land footprint increase further by 0.51 per cent and the level of economic development
decreases the footprint by 0.01 per cent, further. The other driving forces behind the
acceleration of grazing land footprint are population and production of livestock; because, one
percent increase in population and production of livestock, separately increases the grazing
land footprint by 0.15 per cent and 0.02 per cent. The level of urbanization and ecological
efficiency are observed to reduce the grazing land footprint. The coefficient associated with
ecological efficiency is negative and statistically significant. It implies that one percent
increase in ecological efficiency decreases the footprint by 1.90 per cent. Therefore, reductions
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in these factors through different policies can lead to reduce the grazing land footprint and can
achieve the environmental sustainability.
With reference to the built-up land footprint, the empirical findings support the EKC
hypothesis; because the coefficients associated with initial and further levels of economic
development, have expected and statistically significant signs. It implies that initial level of
development increase the built-up land footprint while the further development decreases the
footprint. The population, urbanization and employment forces contribute to increase the built-
up land footprint. However, the impact of urbanization on footprint is greater than population
and employment. It implies that one per cent increase in urbanization, increases the built-up
land footprint by 0.82 per cent and the population and employment impact on footprint are
0.61 per cent and 0.01 per cent, respectively. The service intensity and ecological efficiency
are observed to reduce the built-up land footprint. The acceleration of service intensity and
ecological efficiency would reduce the built-up land footprint. Reduction in population,
urbanization and employment would reduce the built-up land footprint, in the future.
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Table 7.3
The Driving Forces of the Components of Ecological Footprint:
High-Middle Income Countries (Random and Fixed Effect Models)
CO2 footprint Grazing land footprint Built-up land footprint
Independent
Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic
ln(GDP) 1.54* 17.03 0.51* 7.82 0.57* 12.20
ln(GDP2) -0.02* -7.75 -0.01* -3.61 -0.02* -8.50
ln(POP) 0.66* 8.78 0.15* 3.16 0.61* 13.05
ln(UR) 0.42* 3.72 -0.28* -3.21 0.82* 13.35
ln(EI) -0.09* -5.81 - - - -
ln(COAL) 0.05** 1.71 - - - -
ln(OIL) 0.02* 14.26 - - - -
ln(GAS) 0.05* 7.01 - - - -
Ln(MI) 0.19 8.02 - - -0.09* -5.53
ln(HW) 0.002 0.58 - - - -
ln(PL) - - 0.02** 1.94 - -
ln(SI) - - - - -0.18* -6.13
ln(EM) - - - - 0.01* 3.72
Ecological Efficiency -5.28* -2.86 -1.90* -11.05 -1.08* -8.28
Constant -1.13** -1.86 11.81* 8.31 -0.78 -1.15
R-Squared: 0.95
F-Statistic: 215.7* Huasman test: 0.000(0.972)☼
0.96 190.02*
1.31(0.96) ☼
0.93 105.01*
13.31(0.0001) ●
Sample: 2003Q1-2011q4; & Cross-sectional units = 95; periods included=36
Total Panel (Balanced observations) =3420
*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less than
5% supports the FE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
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7.3 The driving forces of total ecological footprint
and its component in high income countries
This section estimates and interprets the impact of driving forces of the total ecological
footprint and its components for the high income countries. Table 7.4 incorporate the impact
of various influencing factors on total ecological footprint. The findings support the EKC
hypothesis because the coefficient associated with GDP is positive and GDP2 is negative. It
implies that further level of economic development reduces the total ecological footprint.
However, the initial stage of economic development increase the total ecological footprint
because the coefficient associated with GDP is positive and statistically significant. It implies
that one per cent increase in economic development leads to 1.92 per cent increases in the total
ecological footprint. Similarly, the coefficient associated with population is positive and
statistically significant as it suggests that one per cent increase in population leads to 0.01 per
cent increase in the footprints, citrus paribus. The results are consistent with York et al. (2004);
Jorgenson and Burns (2007); Anders and John (2009); Mostafa (2010); Torras et al. (2011);
Yong et al. (2013); Al-Mulali et al. (2015); Wei et al. (2015).
The contribution of fossil fuel and urbanization to the ecological footprint is positive and
statistically significant. It implies that one per cent increase in fossil fuel leads to contribute
the total ecological footprint which is 0.01 per cent. The impact of urbanization to footprint
support the modernization perspective. As the society becomes more urbanized, it increases
the material resource use. The findings suggest that one per cent increase in the level of
urbanization, increases the ecological footprint by 0.15 per cent. The findings are consistent
with York and Rosa (2003); Jorgenson and Burns (2007); Anders and John (2009); Ali et al.
(2016) and argue that urbanization of high income countries increased to 882 million in 2000
while it was 703 million in 1975. The projected urbanization in these nations will be 1015
million people living in urban cities in 2030 Behera and Dash (2016). Similarly, the report of
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UN (2014) UN (2014) on urbanization, reveals that 56 per cent of the world’s population lived
in urban areas in 2014, which was 30 per cent of 1950. This leads to increase in demand for
urban activities like urban infrastructures.
Besides, the export, manufacturing, agricultural intensity and the ecological efficiency, the
ecological footprint are also reduced. However, the impact of ecological efficiency on footprint
is larger than the other driving forces, followed by export, agricultural and manufacturing
intensity. It implies that one per cent increase in ecological efficiency decreases the footprint
by 4.31 per cent, citrus paribus. In order to curb the ecological footprint, the appropriate policy
is to promote decoupling process, i.e., the increase in material resource-use should be lower
than the increase in affluence. Regarding the resource productivity, the result is consistent with
Wiedmann et al. (2015).
The negative and statistically significant relationship among export, manufacturing,
agriculture intensity and the ecological footprint suggest that high income countries are trying
to increase through using the environmental friendly technology in agriculture, manufacturing
sectors and the export process zones. One per cent increase in above factors would decrease
the ecological footprint by 0.12 per cent, 0.03 per cent and 0.06 per cent, respectively which
also confirms the arguments of treadmill production theory. It implies that because of favorable
term of trade, high income countries extract resources from less developed countries in the
form of forest; cropland; livestock; and agriculture goods. The result are also in line with
Jorgenson and Burns (2007), the World Bank statistics and Xie et al. (2015), where they argued
that the growth in manufacturing intensity in high income countries showed declining trend
during 2000-2011 because of global financial crises and was extremely high de-growth in
manufacturing intensity in 2008-09.
The manufacturing intensity supports the argument of the World-systems theory and the
theory of uneven ecological exchange. They argued that slower rate in natural resource-use
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and consequently the efficient technology practices in the manufacturing process leads to a
slower increase in the ecological footprint.
The effect of population, urbanization and fossil fuel is positive and statistically significant
and confirmed the ecological modernization perspective. They argued that modernization in
the form of further movement in industrialization, urbanization, unequal trade relation, and
market expansion; leads to increase the total ecological footprint.
The negative effect of service intensity on ecological footprint tends to explain that the
share of service sector in high income countries GDP is continuously increasing, and hence,
increase the consumption of environmentally friendly raw materials.
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Table 7.4
The Driving Forces of Total Ecological Footprint:
High Income Countries( Random Effect Model)
Independent
Variables
Coefficients t-Statistic
ln(GDP) 1.92* 10.80
ln(GDP2) -0.01** -1.62
ln(POP) 0.01** 1.68
ln(UR) 0.15* 5.62
ln(FF) 0.01* 3.53
ln(EI) -0.12* -10.9
ln(SI) -0.01** -0.48
ln(MI) -0.03* -2.57
ln(AI) -0.06 -10.37
ln(IE) 0.01 0.47
Ecological Efficiency -4.31* -12.09
Constant 8.17* 10.21
R-Squared: 0.89 F-Statistic: 209.1* Huasman test : 0.000 (0.967)☼
Sample: 2003Q1-2011Q4; Cross-sectional units = 30; periods included=36
Total Panel (Balanced observations) =1080
*& ** indicate 5 percent and 10percent level of significance. ☼ indicates probability that is greater than 5%, which supports the RE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World
Bank Dataset
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Table 7.5 estimate the determinants of cropland, forest and fisheries footprints. With
reference to the cropland footprint, the findings suggest that driving forces that contribute to
increases the cropland footprint are at initial level of economic development, population,
agricultural intensity, education and consumption of agricultural products. However, the
impact of economic development on cropland footprint is greater, followed by population,
education and agricultural intensity. As, one per cent increase in eco-growth increases the
cropland footprint by 1.57 per cent; and one per cent increase in population, education and
agricultural intensity, increases the cropland footprint by 0.28 per cent, 0.11 per cent and 0.05
per cent, respectively. The results are consistent with the Jorgenson and Burns (2007); Anders
and John (2009); Al-Mulali et al. (2015); Marie and Olivier (2015); Wiedmann et al. (2015),
where they argued that economic development and population are the major driving forces of
the depletion of resources. In addition, the results do not show evidence of inverted U-shape
Environmental Kuznets Curve. The coefficient associated with further level of economic
development is negative but statistically insignificant. It shows that the cropland footprint is
not sensitive with further level of economic development. The result is consistent with (Jill et
al., 2009; Yong et al., 2013). The empirical findings support the negative association between
urbanization, ecological efficiency and cropland footprint. The increase in ecological
efficiency contributes to greater decrease in the cropland footprint. One per cent increase in
ecological efficiency decreases the cropland footprint by 11.30 per cent, ceteris paribus; and
one per cent increase in urbanization decreases 0.61 per cent of the cropland footprint. The
findings support the modernization perspective. As the economy becomes more urbanized, it
reduces the material resources consumption.
Findings of fisheries footprint support the EKC hypothesis. The coefficients associated
with initial and further level of economic development are statistically significant. The initial
level of economic development is greater to contribute and increase the fishery footprints,
because one per cent increase in economic development increases 3.23 per cent of the fishery
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footprint. The further level of economic development reduce fishery footprint by 10 per cent.
The other driving forces that contribute to accelerate the fishery footprint are urbanization and
export of fish and fishery products. The coefficients associated with these factors are positive
and statistically significant. Their corresponding response on fishery footprint is 5.06 per cent
and 0.26 per cent. The improvement in ecological efficiency contribute to reduce the fishery
footprint, because it is negatively affecting it. The coefficient associated with ecological
efficiency shows that one per cent increase in eco-efficiency decreases 10.18 per cent of the
fishery footprint. Thus, the findings suggest that the policy which contribute to increase
economic development and the ecological efficiency would reduce the fishery footprint.
Similarly, the lower dependency on urban activities and fish and fishery products export would
also reduce the fishery footprints into the future.
With reference to the forest footprint, the findings do not support the EKC hypothesis. The
increase in economic development increases the forest footprint. The other driving forces that
contribute to increase in the forest footprint are population and export of primary products. The
response of one per cent increase in these factors contribute to increase the forest footprint by
0.55 per cent and 0.03 per cent, respectively. The contribution of urbanization and ecological
efficiency to forest footprint is negative. The improvement in urban planning activity in light
of friendly environment and the ecological efficiency would reduce the forest footprint. The
findings shows that one per cent increase in ecological efficiency reduces 2.52 per cent of the
forest footprint. the improvement in urbanization reduces the forest footprint by 1.25 per cent.
Thus, the decoupling process, proper utilization of material resources and environment friendly
urban activities, would reduce the forest footprint in the future. The findings also explain that
high income countries are trying to increase the green economies.
The positive effect of affluence on cropland, fisheries and forest footprints also suggests
that because the high income countries are trying to maintain high standard of living, therefore,
130
they try to consume a greater volume of material footprint. The second possible reason to
increase these footprints alongwith affluence increase is due to conversion in choice preference
towards nutrition and wood related material in construction. As explained by GFN (2014);
Perry (2014); Marie and Olivier (2015); Wiedmann et al. (2015) the affluence has increased
material footprints of developed countries, since 1990.
The positive effect of population and urbanization on material footprints suggest that the
demand for the consumption of cropland items and building infrastructure relates to inputs
increase. The positive effect of education on cropland and forest footprints suggest that as
economies mature in term of education, they give less importance to reduce material footprints.
The results are consistent with findings of Jorgenson (2005); Jorgenson and Rice (2005); Hao
et al. (2016) with inclusion of export of primary goods. They argued that income inequality
increases the ecological footprint because of large dependency of export on primary items.
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Table 7.5
The Driving Forces of The Components of Ecological Footprint:
High Income Countries( Random and Fixed Effect Models)
Cropland footprint Fisheries footprint Forest footprint
Independent
Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic
ln(GDP) 1.57* 2.98 3.23* 2.40 2.40* 5.97
ln(GDP2) -0.01 -0.44 -0.10* -2.02 0.005 0.34
ln(POP) 0.28* 5.85 -0.95 -0.96 0.55* 6.76
ln(UR) -0.61* -2.36 5.06* 3.92 -1.25* -5.55
ln(AI) 0.05** 1.76 - - - -
ln(EDU) 0.11* 4.64 - - 0.05 0.71
ln(CA) 0.02* 2.21 - - - -
ln(SF) - - 0.26* 2.21 - -
Ln(EP) - - - - 0.03 1.27
Ecological Efficiency -11.30* -6.01 -10.18* -2.16 -2.52* -15.62
Constant 13.86* 3.73 -5.28* -0.22 29.88* 12.62
R-Squared: 0.98
F-Statistic: 33.01* Huasman test: 0.0000(0.978)☼
0.96 430*
0.0000(1.000)☼
0.54 122.0*
0.0000(0.980) ☼
Sample: 2003Q1-2011Q4; Cross-sectional units = 30; periods included=36
Total Panel (Balanced observations) =1080
*& ** indicate 5 percent and 10percent level of significance. ☼ indicates probability that is greater than 5%, which supports the RE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
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In Table 7.6, determinants of CO2, grazing land and built-up land footprints is empirically
estimated. The results support the U-EKC hypothesis because further level of economic
development increase the CO2 footprint where the GDP is positive. The driving forces like
population, coal, oil, export and manufacturing intensities are positively related to CO2
footprint. One per cent increase in population increases by 0.05 percent CO2 footprint.
Similarly, one per cent increase in the consumption of coal and oil leads to increase CO2
footprint by 0.04 per cent and 0.27 per cent, respectively. Substituting renewable energy for
non-renewable energy would reduce emissions into the future. The export and manufacturing
intensities affect the CO2 footprint, positively, which implies that one percent increase in
driving forces leads to 0.04 per cent and 0.21 per cent increase in CO2 footprint, respectively.
The positive effect of further affluence, population, export, manufacturing, hours’ work
and energy related inputs on CO2 footprint suggest that high income countries have the
experience of a greater increase in CO2 emissions, alongwith consumption of these driving
forces. As economies mature in term of affluence, and economically open in term of export
and manufacturing, they increase work time of employees and the energy consumption. These
factors collectively accelerate the CO2 emissions. The empirical literature further suggest that
high income countries, in addition to inclusion of increase use of energy consumption lead to
assimilate more CO2 emissions. Adding the export and manufacturing intensity does not
change the significance of affluence and energy consumption. The impact of hours work on
CO2 footprint is positive and significant statistically which is consistent with findings of
Anders and John (2009); Hafstead et al. (2015). They argued that the high income countries
reduced the labour hours while achieving greater economic development because of an
increase in labour productivity. This leads to increase the ecological efficiency in high income
countries.
The urbanization fails to increase the CO2 footprint. As economies modernize, they
reduced the CO2 footprint. The expectation of treadmill production perspective is supported by
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existence of the U-EKC hypothesis and the negative effect of urbanization on CO2 footprint.
The improvement in ecological efficiency reduces the CO2 footprint, because the coefficient
associated with ecological efficiency is negative and statistically significant. It shows that one
percent increase in ecological efficiency increases the CO2 footprint by 10.1 percent.
With reference to the grazing land footprint, the findings support the EKC hypothesis.
Among high-income countries, the initial level of economic development increases the grazing
land footprints and the further level of economic development appears to reduce the grazing
land footprint. In addition, acceleration in urbanization, production of livestock and the
ecological efficiency appear to reduce the grazing land footprint. However, the impact of
ecological efficiency on grazing land footprint is larger than from urbanization and the
production of livestock. It shows that one per cent increase in ecological efficiency decreases
the grazing land footprint by 3.64 per cent. The high income countries are decoupling their
economic development and material resource use. The increase in material resource use is
lower than the increase in income. Furthermore, the growth in population increases the grazing
land footprints, because the coefficient associated with population is positive and statistically
significant. It shows that one per cent increase in it leads to an increase 0.30 per cent of the
grazing land footprint.
Findings of the built-up land footprint support the validity for EKC hypothesis. The
coefficients associated with initial further levels of economic development which are
statistically significant. The built-up land footprint among high income countries increase with
the increase in the initial economic development. Further economic development reduces the
built-up land footprint. However, the impact of initial economic development on built-up
footprint is greater than the further economic development. It implies that one per cent increase
in initial level of economic development increases built-up land footprint by1.63 per cent,
citrus paribus. The other driving forces that contribute to increase the built-up footprint are
population, urbanization and employment. The impact of service intensity and the ecological
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efficiency on built-up land footprint is negative and statistically significant. However,
improvement in the ecological efficiency largely decreases the footprint, because one per cent
increase in the ecological efficiency reduces built-up land footprint by 4.97 per cent. Thus,
decoupling in built-up land footprint increases the ecological efficiency and reducing
environmental degradation. The further level of economic development, the policy of
controlling population and increase in rural activities (instead of urbanization) will increase
the environmental sustainability in the form of reducing built-up land footprint. It is consistent
with Dietz et al. (2003); York and Rosa (2003); York et al. (2004). The results support the role
of ecological efficiency in order to reduce grazing and the built-up land footprints.
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Table 7.6
The Driving Forces of The Components of Ecological Footprint:
High Income Countries( Random and Fixed Effect Models)
CO2 footprint Grazing land footprint Built-up land footprint
Independent Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic
ln(GDP) -0.84* -2.40 0.95* 7.78 1.63* 6.67
ln(GDP2) 0.10* 10.11 -0.01 -3.57 -0.05* -4.34
ln(POP) 0.05** 1.79 0.30* 3.61 0.88* 7.86
ln(UR) -0.44 -3.02 -0.45* -2.47 0.87* 3.54
ln(EI) 0.04* 2.01 - - - -
ln(COAL) 0.04* 4.23 - - - -
ln(OIL) 0.27* 8.23 - - - -
ln(GAS) -0.03* -3.10 - - - -
Ln(MI) 0.21* 0.79 - - -0.02 -0.71
ln(HW) 0.40* 3.29 - - - -
ln(PL) - - -0.03 -1.03 - -
ln(SI) - - - - -0.21** -1.96
ln(EM) - - - - 0.16* 2.48
Ecological Efficiency -10.10* -6.92 -3.64* -10.40 -4.97 -9.83
Constant 21.66* 12.22 10.17* 9.61 4.46* 2.78
R-Squared: 0.77
F-Statistic: 332.01* Huasman test: 0.0000(0.970) ☼
0.98 352.2*
39.80(0.000)●
0.62 17.01*
28.18(0.004) ●
Sample: 2003Q1-2011Q4; Cross-sectional units = 30; periods included=36
Total Panel (Balanced observations) =1080
*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less
than 5% supports the FE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
From the above discussion, it is concluded that major driving forces are positively related
to the total ecological footprint and its components are GDP, population and level of
urbanization. The EKC hypothesis confirms that further economic development leads to reduce
the total ecological footprints and its components, except for built-up land footprints. In case
of total ecological footprints, the major driving forces that lead to increase the total ecological
footprints are GDP, population, urbanization, fossil fuel, export and service intensities, and the
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income inequality. In case of CO2 footprint, the major driving forces that are positively
affecting the CO2 footprint are GDP, population, coal, oil, gas and manufacturing intensity.
However, the support of EKC hypothesis is very weak due to the reason that the decoupling
process in high income countries is relatively slow. It is also supported by findings of
Wiedmann et al. (2015);Ozbugday and Erbas (2015), where the high income countries reduce
the use of resources alongwith increase in economic growth. The non-significance and even
positive sign associated with coefficient of economic development, support the arguments of
World-system and Treadmill production theories.
For more profit accumulation, the high level of economic development and consumption
of natural resources will increase and lead to more competition in the global marketplace, as
argued by the world-system theorists. The treadmill of production theorists argue that usually
the producers-base in high countries and expansion of products, largely depend on resources
which are commonly extracted from low income countries. The high income countries
externalize environmental impact of extracting resources of low income countries and
produced commodities are usually transported to and consume by their population. Increase in
economic development further lead to environmental impact through extraction of natural
resources and waste generated by expansion of production. Thus, according to the world-
systems theory and the treadmill of production theory, high income countries generated
consumption based environmental degradation.
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7.4 The driving forces of total ecological footprint
and its component in middle income countries
This section estimate the driving forces of total ecological footprint and its component for
the middle income countries. According to Table 7.7, the results confirm the EKC hypothesis
because one per cent increase in initial level of economic development leads to 1.26 per cent
increase in total ecological footprint; and further level of economic development leads to 0.01
per cent reduction in the ecological footprint. It also explain that middle income countries are
trying to achieve the decoupling process as indicated by the negative and statistically
significant signs of the quadratic of affluence. Further economic development in middle
income countries leads to reduce the ecological footprint. However, decoupling process is very
small and there are many aspects of small decoupling.
First, the middle income countries are using less environmentally friendly technology
where the further economic development require more material inputs. Second, the export of
these countries is either agricultural or manufacturing-based raw materials. Since, the
negligible practices of environmentally friendly technology, further level of development that
is based on the aforementioned sectors leads to increase in the material footprint. Third, the
middle income countries may be unable to execute the material footprint efficiently while
accelerating the economic development due to the lack of invention in resource productivity
and negligible coordination among various institutions. Lastly, as economies modernizing in
term of economic development, they need natural resources and therefore, increase the
ecological footprint.
The other driving forces that contribute to increase in the footprint are population,
urbanization, fossil fuel, service and manufacturing intensity. The coefficients associated with
these forces are positive and statistically significant. However, the population and fossil fuel
appear to contribute largely to the footprint. The findings are consistent with Jorgenson and
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Rice (2005); Jill et al. (2009); York et al. (2009); Knight et al. (2013). The positive and
statistically significant effect of economic development, population, fossil fuel and export
intensity on the total ecological footprint confirms the treadmill production perspective. They
argued that as economies matures in term of acceleration of economic development alongwith
increase in population, therefore they, indeed depend on export and demand of goods and
services. These driving forces collectively and continuously withdraw resources from the
environment and also generate waste. However, the effect of population and fossil fuel on
ecological footprint is more pronounce. Increase in population, demands for crops, fisheries,
grazing and urbanization-based activities that consequently accelerate the material footprint.
An increase in fossil fuel leads to generate CO2 emissions. It is the major contributor to total
ecological footprint (GFN, 2016a). The statistically insignificant effect of export and
agricultural intensity on ecological footprint confirms that in the middle income countries the
process of these activities is very low and therefore, it do not significantly alter their material
footprints. The income inequality fails to increase the ecological footprint because its effect on
footprint is statistically insignificant. However, the decupling process reduce the footprint,
because the ecological efficiency is negatively affecting the footprint. It implies that one
percent improvement in ecological efficiency decreases total ecological footprint by 5.83 per
cent.
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Table 7.7
The Driving Forces of Total Ecological Footprints:
Middle Income Countries( Random Effect Model)
Independent Variables
Coefficients t-Statistic
ln(GDP) 1.26* 6.73
ln(GDP2) -0.01* -9.68
ln(POP) 0.19* 5.48
ln(UR) 0.17* 4.40
ln(FF) 0.16* 12.1
ln(EI) 0.007 0.99
ln(SI) 0.04* 2.28
ln(MI) 0.11* 5.70
ln(AI) 0.01 1.17
ln(IE) -0.04 -1.17
Ecological Efficiency -5.83* -6.81
Constant 8.16* 7.46
R-Squared: 0.82 F-Statistic: 103.01* Huasman test: 0.0000 (0.981)☼
Sample: 2003Q1-2011Q4; Cross-sectional units = 36; periods included=64
Total Panel (Balanced observations) =2340
*& ** indicate 5 percent and 10percent level of significance. ☼ indicates probability that is greater than 5%, which supports the RE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank
Dataset
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Table 7.8, the impact of driving forces of cropland, fisheries and forest land footprints is
estimated. Findings in the case of cropland footprint confirm the EKC hypothesis because the
coefficient associated with GDP2 is negatively affecting the cropland footprint, which implies
that one percent increase in the initial level of economic development increases cropland
footprint by1.42 per cent, while further level of economic development leads to reduce the
cropland footprint. Similarly, the coefficients associated with population, urbanization and
agricultural intensity are positively affecting the cropland footprint. It implies that one per cent
increase in the level of population, urbanization and agriculture intensity increases the cropland
footprint by 0.50, 0.17, and 0.13 per cent respectively. However, the economic development
and population are greater contributors to increase the cropland footprint. There are various
ways through which this result may be justified. Firstly, due to increase in population, demand
for crops’ related items accelerate. Secondly, due to the agriculture based intensity of middle
income countries, increase in economic development leads to increase the cropland footprint.
Thirdly, mostly the middle income countries are largely populated and face the problem of
food security. In order to satisfy the demand of large population and to minimize the deficiency
of food security, they increase the cropland footprint. Lastly, the economic structure in terms
of consumption of material resources does not support the middle income countries, because
they have deficits in cropland footprint. These factors will lead to increase the cropland
footprint.
The negative and statistically significant effect of education and ecological efficiency on
the cropland footprint supports the ecological modernization perspective. As economies
become more urbanized and educated, they try to increase the green economies; therefore, the
net effect of modernization on cropland footprint becomes helpful. The improvement in
resource productivity decreases the cropland footprint.
The positive and statistically significant effect of agricultural intensity on cropland
footprint can be evaluated by the two sides. First, the increasing trend in pesticides and
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fertilizer practices leads to increase in demand for cropland land. Second, due to climate
changes and the increasing trend in population, the middle income countries, are at the stage
of food security problem. They, therefore try to increase the agricultural practices. For this
purpose, some part of reserve biocapacity of cropland has brough t under cultivation and
accelerated the cropland land footprint.
The driving forces that lead to increase fisheries footprints are the further level of economic
development, population, urbanization and fish export. The impact of population on fisheries
footprint is larger than the other driving forces, because one per cent increase in population,
increases further level of economic development, urbanization and fish export by 1.05 per
cent, 0.02 per cent, 0.41 per cent and 0.64 per cent, respectively. However, the findings support
the U-EKC hypothesis because the coefficient associated with GDP2 is positive and GDP is
negative. The driving forces that contribute to decrease the fisheries footprint are at initial level
of economic development and the ecological efficiency. It shows that the response of one per
cent in these factors decreases fisheries footprint by 0.22 per cent, 1.71 per cent and 0.13 per
cent. The results are consistent with Jorgenson and Clark (2011); Knight et al. (2013); Apergis
and Ozturk (2015). Fisheries footprint is one of the most severed issues because of its
unprecedented economic development and population explosion. The effect of economic
activity is the largest positive contributor to accelerating the fisheries footprint, followed by
population and diet structure effects. The combination of fisheries footprint efficiency and
adjustment in the structure of dietary practices are the most effective approach for controlling
fisheries footprint. With a growing population and recurrent problems of food security, the
middle income countries also leads to accelerate the consumption of fishery resources.
The driving forces contribute to accelerate the forest footprint are economic development,
population and the export of primary products. Further level of economic development,
urbanization and the ecological efficiency contribute in reduction in forest footprint. The
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findings support the EKC hypothesis, because the coefficients associated with economic
development and its further level, are statistically significant. The initial level of economic
development increase the forest footprint by 2.41 per cent in case of one per cent increase in
economic development. Among the middle income countries, one percent growth in income
decreases forest footprint by 0.08 per cent. Among the middle income countries, the
acceleration in the export of primary products increases 0.03 per cent of the forest footprint.
Majority of the middle income countries have the increasing trend in population that
contributes to increase in the forest footprint by 0.26 per cent. The improvement in
urbanization and the ecological efficiency decreases the forest footprint by 1.60 per cent and
4.20 per cent, respectively. As the society becomes more urbanized and increase the
decoupling process in resource productivity, it try to increase the forest reserve. The findings
are consistent with Jorgenson and Burns (2007); Jill et al. (2009); Mostafa (2010); Jorgenson
and Clark (2011); Alessandro et al. (2012); Juan and Jordi (2013); Marie and Olivier (2015);
Wei et al. (2015); Wiedmann et al. (2015); Ali et al. (2016); Asici and Acar (2016).
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Table 7.8
The Driving Forces of The Components of Ecological Footprint:
Middle Income Countries(Random and Fixed Effect Models)
Cropland footprint Fisheries footprint Forest footprint
Independent
Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic
ln(GDP) 1.42* 5.11 -0.22** -1.67 2.41* 21.17
ln(GDP2) -0.03* -6.58 0.02* 6.58 -0.08* -11.09
ln(POP) 0.50* 6.53 1.05* 6.51 0.26* 2.24
ln(UR) 0.17** 1.65 0.41** 1.83 -1.66* -10.4
ln(AI) 0.13* 7.05 - - - -
ln(EDU) -0.05* -3.87 - - 0.01 0.70
ln(CA) -0.01* -0.45 - - - -
ln(SF) - - 0.64* 23.91 - -
ln(EP) - - - - 0.03* 4.68
Ecological Efficiency -4.80* -18.62 -1.71* -3.31 -4.20* -7.20
Constant 4.37* 4.13 -6.60** -2.94 9.28* 5.96
R-Squared: 0.97
F-Statistic: 117.3* Huasman test: 43.85(0.000)●
0.98 369.6*
79.77(0.000)●
0.66 106.1*
0.0000(0.980) ☼
Sample: 2003Q1-2011Q4; Cross-sectional units = 36; periods included=64
Total Panel (Balanced observations) =2340
*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less than
5% supports the FE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
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Table 7.9 reports the driving forces of CO2 footprint and the grazing and built-up land
footprints. With reference to the determinants of CO2 footprint, the factors contributing to its
increase are the initial level of economic development, coal, oil, gas, manufacturing and work
hours. However, the greater contributors to CO2 footprint are the economic development and
oil. The results show that one per cent increase in these factors contribute to increase CO2
footprint by 2.14 per cent and 0.44 per cent respectively. On the other hand, the urbanization
and the ecological efficiency have quite strong implication for the mitigation of CO2 footprint:
one per cent increase in ecological efficiency reduces the CO2 footprint by 7.99 percent, ceteris
paribus. A one percent increase in the urbanization decreases CO2 footprint by 0.04 percent.
The findings support the EKC hypothesis, because coefficient associated with and further level
of economic development is negative and statistically significant. At the initial level of
economic development, CO2 footprint increases alongwith the growth in income’ but however,
at the further level of economic development, the CO2 footprint decreases. Thus, the policy
makers in the middle income countries should have to pursue sustainable policies regarding
decoupling, growth in income and environmental friendly urbanization activities in order to
reap the maximum environmental sustainability.
The empirical estimate of driving forces of grazing land footprint and the result confirm
the EKC hypothesis negative, but the GDP is positive, because of the coefficient associated
with GDP2. It implies that an increase in GDP leads to increase in grazing land footprint and
further economic development (GDP2) leads to reduce grazing land footprint of middle income
countries. However, the factors like population and production of livestock are positively
related to grazing land footprints which imply that one percent increase in these factors leads
to 0.31 per cent and 0.02 per cent increase in grazing land footprint, respectively. The other
driving forces that contribute to reduce the grazing land footprint are the level of urbanization
and the ecological efficiency. However, the effect of ecological efficiency on grazing land
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footprint is more pronounced than urbanization. It shows that one percent increase in
ecological efficiency reduce grazing land footprint by 3.54 per cent. Thus, improvement in
ecological efficiency, urbanization and controlling population and decoupling the resources
productivity would increase the environmental sustainability by reducing the grazing land
footprint.
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Table 7.9
The Driving Forces of The Components of Ecological Footprint:
Middle Income Countries( Random Effect Model)
CO2 footprint Grazing land footprint Built-up land footprint
Independent
Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic
ln(GDP) 2.14* 11.3 1.01* 5.77 -2.75* -5.41
ln(GDP2) -0.04* -3.98 -0.01* -2.27 0.21* 6.78
ln(POP) 0.08* 7.09 0.31* 2.18 0.51* 17.41
ln(UR) -0.42* -14.02 -0.71* -3.51 0.06* 5.34
ln(EI) -0.009 -0.48 - - - -
ln(COAL) 0.05* 19.33 - - - -
ln(OIL) 0.44* 12.23 - - - -
ln(GAS) 0.06* 13.05 - - - -
ln(MI) 0.08** 1.71 - - -0.96* -7.02
ln(HW) 0.02* 7.27 - - - -
ln(PL) - - 0.02* 3.16 - -
ln(SI) - - - - -1.58* -8.34
ln(EM) - - - - 0.03* 4.43
Ecological Efficiency -7.19* -7.48 -3.54* -7.86 -3.38* -12.27
Constant 5.82** 1.67 10.58* 5.54 7.51* 12.86
R-Squared: 0.96
F-Statistic: 594.3* Huasman test : 0.0000(0.960) ☼
0.98 375.3*
1.75(0.914) ☼
0.71 996.1*
10.70(0.219) ☼
Sample: 2003Q1-2011Q4; Cross-sectional units = 36; periods included=64
Total Panel (Balanced observations) =2340
*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model.
Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset
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The association of built-up land footprint with GDP and its square term, population,
urbanization, manufacturing and service intensity; and employment level are empirically
estimated. The findings confirm the U-EKC hypothesis because the further level of economic
development increases the built-up land footprint. The other driving forces that contribute to
increase the built-up footprint are population, urbanization and employment. However, the
effect of population on built-up footprint is more pronounced because, one per cent increase in
population sparks a 0.051 per cent rise in built-up footprint. The employment leads to a small
increase in built-up footprint than the level of urbanization. From the estimation, one per cent
increase in these factors lead to increase in the built-up footprint by 0.06 per cent and 0.03 per
cent, respectively. The service intensity and ecological efficiency gives the negative effect on
by the built-up footprint as does the economic development. However, they are strongly
mitigated the built-up footprint. As, a 1 percent improvement in ecological efficiency and
increase in service intensity leads to reduce the built-up footprint by 1.58 per cent and 3.38 per
cent, respectively. The findings strongly support the decoupling in growth and modernization
perspective in terms of service and manufacturing activities. The manufacturing intensity gives
the same effect on the built-up footprint as the service and ecological efficiency. The response
of one per cent increase in manufacturing intensity reduces built-up footprint by 0.96 per cent,
ceteris paribus.
From the above discussion, it is concluded that driving forces where they have positive
impact on total ecological footprint and its components are economic development and
population. The major driving forces which increase the total ecological footprint are economic
development, population, fossil fuel and export intensity. In case of cropland footprint, the
economic development, population and agriculture intensity are positively affecting the
cropland footprint while the further level of economic development, urbanization and
education are negatively related to cropland footprint. Similarly, the major driving forces that
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lead to increase the fisheries footprint are economic development, population and export of
fish while urbanization is related to fisheries footprint, negatively. In case of forestland
footprint, the driving forces are economic development, population, urbanization, education,
export intensity and income inequality. The economic development, population, urbanization,
export intensity and income inequality are positively, while education is negatively, related to
forestland footprint. Similarly, the driving forces of CO2, grazing and built-up land footprints
have positive effects on CO2 footprint, grazing and built-up land footprints. However, the
findings support the EKC hypothesis in case of total ecological footprint and its components
which shows that initial level of economic development lead to increase ecological footprint
and then, further level of economic development reduce the ecological footprint.
7.5 Discussion
From the above discussion, it is concluded that major driving forces that contribute to
ecological footprint and its components are not only the population and affluence but many
other factors, as suggested in findings of the study.
With reference to the combined panel, findings have several merits for different policy
makers. Firstly, the combined panel (high-middle income countries) should slow the process
of population, fossil fuel, urbanization and different intensities in order to combat global
environmental pressure. These driving forces increase the total footprint and therefore, slowing
the impact of these on ecological footprint could lead to maintain the environmental
sustainability. Secondly, the findings of total ecological footprint suggest that improvement in
ecological efficiency and the promotion of export intensity in light of pro-environment would
improve the environmental sustainability by reducing the ecological footprint. Thirdly, the
major driving forces that contribute to increase the cropland footprint are affluence, population,
export intensity and the consumption of agricultural products. It is suggested that decoupling
in growth in income, promotion of export and agricultural substitute products would accelerate
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the cropland biocapacity. Fourthly, further level of economic development and ecological
efficiency should be promoted, because these lead to mitigate the cropland footprint. Fifth, the
combined panel should slow the consumption of fish and fishery products, because it accelerate
the fishery footprint. The fisher footprint has the potential of decoupling process. Sixth, the
findings of forest footprint suggest that combined panel should reduce the influence of income,
population, education and the export of primary products. The incremental increase in these
factors increases the forest footprint. Lastly, results of the CO2 footprint show that the process
of increasing population, urbanization, coal, and oil and gas consumption should slow down.
It is possible to introduce environmental friendly technology by the sample countries in the
manufacturing, infrastructure and transport sectors.
By comparing the findings of high and middle income countries with reference to total
ecological footprint; the results support the EKC hypothesis. Initial level of economic
development increase the footprint but further level of economic development reduce the total
ecological footprint. However, the decoupling process is greater in middle income countries
than high income countries, as suggested by the ecological efficiency. The improvement in
ecological efficiency has greater impact on ecological footprint in case of middle income
countries. The other driving forces that contribute to increase the footprint are population,
urbanization and fossil fuel. However, they have greater impact on ecological footprint in the
middle income countries. The reason behind this is the increasing trend of population,
industrialization and urbanization process. For example, urbanization in China expanded from
19.8 per cent (in 1979) to 53.7 per cent (in 2013) and CO2 emissions per capita increased from
1.7 tons to 8.3 tons, in the same period. Thus, there was a positive association among these
factors and the ecological footprint (IEA, 2015; Zi et al., 2016). Similarly, the GDP and export
intensity increase the total ecological footprint which may support the Heckscher-Ohlin trade
theory, i.e., without considering the environmental impact of trade, a country should specialize
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in the production of goods which requires the abundant factors. The coefficient associated with
income inequality revealed that increase in inequality will lead to increase ecological footprint
and thus, suggesting that there should be more equal distribution in income which will lead to
reduce the total ecological footprint of these nations. The coefficient associated with service
intensity suggests that increase in service intensity will lead to reduce its ecological footprint
which support the modernization perspective that conversion from agriculture based to service
based economy leads to reduce environmental degradation. Thus, it supports that these nations
should increase the service based activities.
The similar driving forces that contribute to increase the cropland footprint are affluence,
population and agricultural intensity. Since, the high standard living of high income countries
leads to greater effect of income on the footprint than middle income countries. However, the
increasing trend of population and the greater dependency on agriculture activities of middle
income countries have lager influence on the cropland footprint. The greater the ecological
efficiency the lower would be the cropland footprint, because the negative association between
ecological efficiency and the footprint. In addition, the findings also support the EKC
hypothesis which suggests that more focus on controlling population, reducing dependency on
agriculture as well as increasing further the economic development and more investment in
education sector will lead to reduce the cropland footprint of these countries.
Similarly, findings of the fishing grounds concluded that GDP, population and fish export
increase the fisheries footprint in case of high and middle income countries, thus suggesting
that de-growth, population control and minimizing dependency on export of fisheries related
activities will lead to reduce the fishing grounds footprint. The study concluded that
urbanization increase the fisheries footprint in high income countries, though it is insignificant
while it is negatively related to the fisheries footprint in middle income countries and
statistically significant. It leads to suggest that there should be more focus on better urban
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planning which will lead to reduce fishing grounds footprint because of substitute’s availability
for fish in middle income countries. The findings also confirm the EKC relationship of GDP
and GDP2 with fishing grounds footprint.
From the previous discussion, regarding forest footprint for high and middle income
countries it can be concluded that an increase in GDP, population, urbanization, export and
income inequality lead to increase the forest footprint. Education is negatively affecting the
forest footprint and therefore more investment in this sector could lead to reduce the
environmental degradation via reduction of forest footprint of these countries. Similarly,
reduction in urbanization through appropriate rural development policy, as well as decline in
export of forest related products like papers and wood furniture, could also lead to reduce the
forest footprint.
The major driving forces after increasing the CO2 footprint of these countries are affluence,
population, export intensity, consumption on coal, oil and gas, manufacturing intensity and
work hours. Therefore, reduction in these driving forces through different policies, for
example, adaptation of alternative sources for energy consumption like solar, wind and micro-
hydro power systems could lead to reduce the CO2 footprint of the these countries. Similarly,
reduction in working hours through tax on longer working hours could lead to improve the
environmental sustainability via reducing the CO2 footprint; the result are also supported by
the findings of (Anders and John, 2009).
Findings of this study can contribute to the existing literature regarding the grazing land
footprint. The findings show that population, affluence and production of livestock are the key
determinants of these nations’ grazing land footprint and therefore reduction in these forces
will lead to reduce the grazing land footprint. However, the effect of urbanization and
affluence on grazing land footprint in middle income countries is larger than the other driving
forces and therefore, proper urban planning and the de-growth policy would lead to reduce
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these nations’ grazing land footprint. Similarly, the high income countries would lead to reduce
their grazing land footprint by initiating well urban planning. The ecological efficiency is one
of the leading component that contribute to reduce the grazing land footprint. The grazing land
footprint increases and the findings support the EKC hypothesis at the initial level of economic
development,. The further level of economic development reduce the grazing land footprint.
Findings of the built-up land footprint suggested that population; affluence, urbanization,
service intensity and employment level increase the built-up land footprint. However, it is
suggested that proper urban development, creating employment opportunities in rural sector in
the middle income countries could lead to reduce its built-up land footprint. Similarly, further
affluence and urbanization in case of high income countries are the key determinants which
have larger impact on built-up land footprint; therefore, reduction in these sectors would lead
to improve environmental sustainability. The improvement in the ecological efficiency reduces
the built-up land footprint.
Our findings are coherent with Wiedmann et al. (2015) results. They used material
footprint and the domestic material consumption as consumption-based environmental impact
indicators. The indicators are then divided into crops, fodders, ores, construction of materials
and fossil fuel categories. The study investigate the trend over time, resource productivity and
econometric analysis. The trend of material footprint and domestic material consumption over
time indicate that the postindustrial economic structure and the import dependency for final
consumption in the United Kingdom and Japan leads to greater material footprint than the
domestic material consumption. The domestic material consumption of large resource exporter
countries of Australia, Russia and South Africa was greater than their material footprint. The
Brazil, India and China have a similar material footprint and domestic material consumption
over time. They further argued that specialization leads to change the structure of resources
extraction, particularly the domestic material consumption, because its value increased for
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exporting and decline in importing countries. But, specialization have less effect on material
footprint because it reallocates the burden back to the ultimate point of consumption.
The resource productivity in the case of selected developed countries has no decoupling
process. The main reason was greater dependency on construction material. The fast-growing
economies of China and India shows a result of decoupling. The exporting developing nations
of Chile, Brazil and Russia had a decline trend in resource productivity. The univariate
regression analysis based on three explanatory variables indicate the mixed findings of the
impact of GDP, DE and population density forces on material footprint. They argued that
variation in material footprint is mostly explained by variation in GDP and thus increase in
resource productivity is smaller than the increase in GDP. They estimated that GDP leads to
increase the footprints of selected countries which is consistent with results of this study; in
the case of analysis of high and middle income countries. The population density seems to
have a lesser and mixed influence on footprint indicators. An increase in population density
leads to a positive impact on total footprint, crops, and construction materials, as well as on
the domestic material consumption of crops, fodder, construction and fossil fuels. Variation in
domestic material consumption is mostly explained by variation in the domestic extraction and
had less impact by GDP in the domestic material consumption. The GDP increase the
construction component of material footprint, but not by the domestic extraction. It shows that
10 per cent increase in GDP, increase the construction footprint by 9 per cent. In response of
10 per cent increase in domestic extraction, variable leads to increase construction component
of domestic material consumption by 8 per cent. Findings of this study explain that a further
level of economic development leads to increase cropland footprint.
The findings of Wiedmann et al. (2015); Ali et al. (2016); Asici and Acar (2016); Chen et
al. (2016); Farhani et al. (2016); Kang et al. (2016); Li and Zhao (2016); Shi-Chun et al.
(2016); Uddin et al. (2016); Adewuyi and Awodumi (2017); Ahmad et al. (2017); Apergis et
al. (2017); Charfeddine and Mrabet (2017); Fernandez-Amador et al. (2017); Lanouar (2017);
154
Mrabet and Alsamara (2017); Szigeti et al. (2017) strongly support the hypothesis of this study
as the initial level of economic development increases the ecological footprint and
environmental degradation. One of the major conclusion from the findings is the improvement
in ecological efficiency and that decoupling in growth in income reduces the ecological
footprint.
Zhu et al. (2016); Zaman and Abd-el Moemen (2017) results support the findings (of this
study) that growth in income reduces the emissions and growth in population increases the
environmental degradation. The results therefore, support the EKC and the IPAT hypotheses.
However, Uddin et al. (2017) Uddin, et al. (2017) argued that real income confirms the positive
significant impact on the ecological footprint in case of highest emitting countries. They further
argued that there is a unidirectional causal impact running from real income to ecological
footprint. As, our findings suggest the growth in initial income increase the ecological footprint
in high-middle income countries. The reports of GFN (2014, 2016a) support the findings of
this study and that high economic development, population, urbanization and fossil fuel
consumption are the major driving forces that contribute to increase the footprint. However,
increase in ecological footprint is lower than the increase in income which leads to reduce the
environmental degradation, because the ecological efficiency is negatively affecting the
ecological footprint in high-middle income countries.
The findings of Ozbugday and Erbas (2015) are supporting the relationship between
ecological efficiency, ecological footprint and components of ecological footprint. They
argued the increase in energy efficiency reduces CO2 emissions. Increasing energy efficiency
in renewable energy usage is increasing energy efficiency. It reduces CO2 emissions. The IEA
(2015) report also supports the effect of ecological efficiency on environment. Increasing
energy efficiency reduces CO2 emissions. However, the results of Zaman and Abd-el Moemen
(2017) indicate that increase in population pressure increases CO2 emissions and supports the
155
IPAT hypothesis. The study of Wang et al. (2017) employed STIRPAT model to examine the
impact of population, income, energy intensity and urbanization on CO2 emissions. They
argued that the major contributors to CO2 emissions are the intensity of population, income
and energy. Impact of urbanization is ambiguous, because its effect on CO2 emissions differs
in different regions of China. However, the results of study are according to the study of Wang
et al. (2017). The driving forces that contribute to increase CO2 footprint are population,
income and fossil fuel consumption. Urbanization increases CO2 footprint in case of combined
panel.
The important conclusion of this study is the importance of ecological efficiency in
environmental sustainability. Separate and combined samples results show that improvement
in ecological efficiency reduces ecological footprint. Therefore, the decoupling process and
growth in income can improve environmental sustainability. The study of Uddin et al. (2017)
examined the relationship among ecological footprint, real income, financial development and
trade openness for a panel of developed and developing countries, for the period 1991-2012.
They argued that real income increases footprint and financial development reduces ecological
footprint. The 27 highest emitting countries are excessively exploiting natural resources and
eco-services. This is according to the results of our study. Improve ecological efficiency,
change the consumption patterns of people of high-middle income countries, controlling
excessive exploiting of fishing, crops, grazing, forest and built-up land will increase
biocapacity and will promote environmental sustainability. These findings are also compatible
with FAO (2016); GFN (2016a, 2016b); Szigeti et al. (2017).
156
CHAPTER EIGHT
CONCLUSION AND RECOMMENDATIONS
8.1 Introduction
This chapter is devoted to the brief summary of the study, followed by key findings and
recommendations based on these findings. Besides, the limitations of this study; directions for
future research are also mentioned in this part of the study.
8.2 Summary of the study
Since last few decades, ecological footprint is one of the growing area of interest in the
emerging fields like environmental-sociology, environmental-economics and ecological
economics. It is the consumption based environmental impact indicator which measure the
area of land required for consumption of goods and services demanded by human to assimilate
CO2 emissions and waste generated by human activities. This includes all cropland, forest,
grazing, fishing grounds and built-up land to produce food, timber, fiber and to
accommodate/provide space for the urban activities and for assimilation of CO2 emissions.
This study try to provide answers regarding linkages between ecological footprint, economic
growth, population, urbanization and other socioeconomic factors. The study estimated trends
of ecological footprint, economic growth and ecological efficiency; and the inequality between
income, ecological footprint and environmental impact intensity. The impact of various driving
forces of total ecological footprint and its components for high and middle income countries
also estimated this by using Panel dataset for the period 2003-2011. The key findings of the
study are:
1. The total ecological footprints of high income countries showed a mixed trend during
2003-2011 because of mixed trend in CO2 and other components of total ecological
157
footprint. The total ecological footprints of middle income countries increased in same
period due to increase in demand of cropland, grazing land and CO2 footprints. Demand
for area of land to assimilate CO2 emissions generated by high income countries is 59
percent and 43 percent of total ecological footprints of middle income countries during
2003-2011.
2. Comparing total ecological footprints with its biocapacity reveals that mean footprints of
both regions are more than its biocapacity that leads to generate ecological overshooting
of 94 percent and 14 percent in high and middle income countries respectively. The result
suggests that during 2003-2011, high income countries consumed resources and services
at much faster rate than the middle income countries.
3. With reference to coal, oil and gas consumption, the result suggests that both high and
middle income countries have increasing trend of consumption. The consumption of coal,
oil and gas is much larger in high income countries and therefore, demand of ecological
footprints exceeds supply of biocapacity by 94 per cent (ecological overshooting) during
2003-2011.
4. Comparing population and annual hours worked per worker, reveals that middle income
countries have larger population and work hours but it does not lead to explain that people
of middle income countries consume resources and services at much faster rate. Although
on basis of income and urbanization it can conclude that high income countries consume
resources and services at much faster rate and therefore have larger demand of ecological
footprints than the middle income countries.
5. Comparing trend of export, agriculture, manufacturing and service, the intensity reveals
that middle income countries mainly depend on export, agriculture and manufacturing
sectors. While high income countries depend on services. The result suggests that high
income countries try to increase service based activities in order to maintain its
biocapacity more than ecological footprint. Middle income countries, at the stage of
158
development, devote their resources into agriculture and manufacturing sectors that will
lead to increase demand of ecological footprint, much faster than supply of biocapacity
in near future.
6. According to trends of ecological footprint, economic growth and ecological efficiency;
the result suggest that because of the increasing trend in GDP, ecological efficiency of
high income countries was more than the middle income countries, during 2003-2011.
7. The Resource Intensity (RI) exhibits the gap between maximum and mean level of
efficiency. The result suggest that the high and middle income countries have
discrepancy in the case of resources utilization because the cropland, forest and grazing
land have 49 percent and 42 percent potential to achieve its maximum level of ecological
efficiency by high and middle income countries respectively. The CO2 footprint by these
regions have 25 percent and 21 percent more room for achieving maximum level of CO2
ecological efficiency. Similarly, the fishing grounds and built-up land of high and
middle income countries have 25 percent and 37 percent potential for achieving its
maximum level of efficiency.
8. The Atkinson index of total ecological footprint, cropland, grazing land, forest, fishing
grounds and built-up land footprint in High income countries was greater than the Middle
income countries; while the Atkinson index of CO2 footprint in Middle income countries
was greater than the High income countries. Similarly, the Atkinson index of
environmental impact intensity of High income countries was larger than the Middle
income countries, in case of grazing land, forest, fishing grounds, built-up land and CO2
footprint. According to the mean environmental impact intensity, the mean forest, CO2,
fishing grounds and built-up land footprint intensity of per unit economic output in High
income countries is greater than the Middle income countries; while cropland and grazing
land footprint environmental impact intensity of per unit of economic output in Middle
income countries are larger than the High income countries.
159
9. The present study also estimate the impact of various driving forces of the total
ecological footprint and its components where results in case of total ecological footprint
suggest that the major driving forces that leads to affect the total ecological footprint are
GDP, population, urbanization, fossil fuel, export and service intensities and income
inequality. In case of CO2 footprint, the major driving forces that are positively affecting
the CO2 footprint are GDP, population, coal, oil, gas and manufacturing intensity.
10. The econometric findings of the middle income countries suggest that major driving
forces which lead to increase in the total ecological footprint are economic development,
population, fossil fuel and export intensity. In case of cropland footprint, the economic
development, population and agriculture intensity are positively affecting the cropland
footprint; while the further level of economic development, urbanization and education
are negatively related to cropland footprint. Similarly, the major driving forces that lead
to increase fisheries footprint are economic development, population and export of fish
while urbanization is negatively related to fisheries footprint. In case of forestland
footprint, the driving forces are economic development, population, urbanization,
education, export intensity and income inequality. The economic development,
population, urbanization, export intensity and income inequality are positively related;
while education is negatively related to forestland footprint. Similarly, the CO2, grazing
and built-up land footprints are positively affected by GDP and population. However,
the findings support the EKC hypothesis except in the case of built-up land footprint for
middle income countries and the CO2 emission footprint, in case of high income
countries which support the validity of U-EKC hypothesis.
In the light of the above findings, it is concluded that high income countries experienced
greater ecological overshooting than the middle income countries, in the period 2003-2011.
They have experienced greater consumption of coal, oil and gas and have service based
160
activities than the middle income countries. They have discrepancy in utilization of resources
and impacts of various driving forces of the total ecological footprint and its components,
alongwith the discrepancy in inequality and mean environmental impact intensity.
161
8.3 Policy recommendations
Based on the findings of this study, it is concluded that our earth is at the edge of finite
resources but the possibilities are not restricted. Therefore following policies options are
recommended:
1. Findings of the study, reveals that total ecological footprint of High and Middle income
countries is greater than its biocapacity which leads to generate 94 per cent and 14
ecological overshooting in High and Middle income countries, respectively. Therefore,
it is suggested that total ecological footprint of these nations should be reduced, at least
its biocapacity level, which would be possible by matching cropland, forest, grazing
land, fishing grounds and built-up land footprint, with its respectively regenerating
capacity, in a given year.
2. The findings suggest that the level of education is positively related to the total
ecological footprints; therefore, greater investment should go to education sector.
3. The findings reveal that urbanization is positively affecting the built-up land footprint
in High and Middle income countries. It is therefore suggested that there should be
proper planning for rural development (for example creating job opportunities, agro-
based business activities and small scale industries) which will reduce the built-up land
footprint.
4. The findings suggest that fossil fuel, particularly coal and oil, is the major driving
forces which largely increase CO2 ecological footprint. Production and use of
renewable energy alternatives like wind, solar system and micro-hydro power plants
can lead toward environmental sustainability.
5. In their environmental agenda, the high and middle income countries should keep the
utilization efficiency of cropland, forest and grazing land at the first priority; because
they have more potential for achieving its maximum level of efficiency. It would be
162
followed by CO2 footprint, built-up land and fishing ground footprints; otherwise, if
they follow the traditional process of economic development described by high
investment, high growth and low benefit, the utilization of resources will not meet their
need and the environment. It will also be difficult to support their rapid development.
6. The findings of inequality suggest the larger inequality in income, environmental
intensity, total ecological footprint and its components which further increase the
consumption of finite resources/biocapacity and increase the environmental
degradation of globe. Therefore, the High income countries should reduce the forest,
CO2, fishing grounds and built-up land footprint because its mean environmental
impact intensity is greater than biocapacity. Middle income countries should reduce
cropland and grazing land footprint due to its larger mean environmental impact
intensity than the High income countries.
7. The major driving forces that lead to increase cropland footprint are population, GDP,
and agriculture intensity and therefore, suggest that de-growth, population control
policy and conversion from agriculture to service based activities will curtail cropland
footprint.
8. The high and middle income countries should consider the energy usages while
formulating environmental policies because coal, oil and gas consumption increases
CO2 footprint.
163
8.4 Limitations and directions for future research
The limitations and directions for future research are:
1. The methods used by this study in the trend analysis, inequality and gap of resources
consumption can easily be applied to a national or local level to provide the availability
of appropriate data.
2. The separate study for each nation would be more appropriate if time series data is
available. Conducting this type of analysis would provide policy guidelines for a
country understanding of how and to what extent its local activities degrade the
biocapacity.
3. Another area for future research may be possible, by analyzing resource use-ecological
deficit nexus and projection of total ecological footprint where time series data is
available.
164
APPENDIX A
Trend between High and Middle Income Countries Resources Consumption and
Socioeconomic factors
Figure 1A
Trend between High and Middle Income Countries: Coal consumption, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
Figure 2A
Trend between High and Middle Income Countries: Oil consumption, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
0
1
2
3
4
5
6
7
8
9
2001 2003 2005 2007 2009 2011
tho
usa
nd
mil
lio
n t
ons
Year
HIC Coal ConsumptionMIC Coal Consumption
0
2
4
6
8
10
12
14
16
2001 2003 2005 2007 2009 2011
Tho
usa
nd
bar
rels
per
day
Year
HIC Oil ConsumptionMIC Oil Consumption
165
Figure 3A
Trend between High and Middle Income Countries: Gas consumption, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
Figure 4A
Trend between High and Middle Income Countries: GDP per capita, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
0
2
4
6
8
10
12
14
16
18
20
2001 2003 2005 2007 2009 2011
Tho
usa
nd
Bil
lio
n C
ub
ic F
eet
Year
HIC Gas ConsumptionMIC Gas Consumption
0
500
1000
1500
2000
2500
3000
3500
4000
2001 2003 2005 2007 2009 2011
in M
illi
on $
Year
HIC GDP per capitaMIC GDP per capita
166
Figure 5A
Trend between High and Middle Income Countries: Population, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
Figure 6A
Trend between High and Middle Income Countries: Urban Pop., 2003-11
Source: Author Computation based on World Bank data set, 2003-11
0
1000
2000
3000
4000
5000
6000
2001 2003 2005 2007 2009 2011
in M
illi
on
Year
HIC PopulationMIC Population
0
200
400
600
800
1000
1200
2001 2003 2005 2007 2009 2011
Mil
lio
ns
of
tota
l P
op
.
Year
HIC Urban Pop.
MIC Urban Pop.
167
Figure 7A
Trend between High and Middle Income Countries: Working Hrs per employee, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
Figure 8A
Trend between High and Middle Income Countries: Export, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
1700
1750
1800
1850
1900
1950
2000
2050
2001 2003 2005 2007 2009 2011
Per
em
plo
yee
Year
HIC Working Hrs
MIC Working Hrs
0
5
10
15
20
25
30
35
40
2001 2003 2005 2007 2009 2011
% o
f G
DP
Year
HIC ExportMIC Export
168
Figure 9A
Trend between High and Middle Income Countries: Service intensity, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
Figure 10A
Trend between High and Middle Income Countries: Manufacture intensity, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
Figure 11A
Trend between High and Middle Income Countries: Agriculture intensity, 2003-11
Source: Author Computation based on World Bank data set, 2003-11
0
10
20
30
40
50
60
70
80
2001 2003 2005 2007 2009 2011
% o
f G
DP
Year
HIC Service Intensity
MIC Service Intensity
0
5
10
15
20
25
30
35
2001 2003 2005 2007 2009 2011
% o
f G
DP
Year
HIC Manufacture IntensityMIC Manufacture Intensity
0
2
4
6
8
10
12
14
2001 2003 2005 2007 2009 2011
% o
f G
DP
Year
HIC Agr. IntensityMIC Agr. Intensity
169
APPENDIX B
Trend between Ecological Footprint and Biocapacity
Figure 1B
Trend between Ecological footprint and Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 2B
Trend between Cropland footprint and its Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
0
1
2
3
4
5
6
7
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Ecological FootprintBiocapacity
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Cropland Footprint
Cropland Biocapacity
170
Figure 3B
Trend between Grazing land footprint and its Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 4B
Trend between Forest footprint and its Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 5B
Trend between Fishing ground footprint and its Biocapacity: High Income Countries, 2003-
11
Source: Author Computation based on GFN data set, 2003-11
0
0.2
0.4
0.6
0.8
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er
cap
ita
Year
Grazing land FootprintGrazing land biocapacity
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Forest Footprint
Forest biocapacity
0
0.1
0.2
0.3
0.4
0.5
0.6
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er
cap
ita
Year
Fishing Grounds Footprint
Fishing grounds biocapacity
171
Figure 6B
Trend between Built-up land footprint and its Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 7B
Trend between CO2 footprint and Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 8B
Trend between Ecological footprint and Biocapacity: Middle Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
0
0.05
0.1
0.15
0.2
0.25
0.3
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Built-up land Footprint
Built-up land biocapacity
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Co2 FootprintBiocapacity
0
0.5
1
1.5
2
2.5
3
3.5
4
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Ecological Footprint
Biocapacity
172
Figure 9B
Trend between Cropland footprint and its Biocapacity: Middle Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 10B
Trend between Grazing land footprint and its Biocapacity: Middle Income Countries, 2003-
11
Source: Author Computation based on GFN data set, 2003-11
Figure 11B
Trend between Forest footprint and its Biocapacity: Middle Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
0
0.2
0.4
0.6
0.8
1
1.2
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er
cap
ita
Year
Cropland FootprintCropland biocapacity
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Grazing land FootprintGrazing land biocapacity
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Forest FootprintForest biocapacity
173
Figure 12B
Trend between Fishing grounds footprint and its Biocapacity: Middle Income Countries,
2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 13B
Trend between Built-up land footprint and its Biocapacity: High Income Countries 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 14B
Trend between CO2footprint and Biocapacity: High Income Countries, 2003-11
Source: Author Computation based on GFN data set, 2003-11
0
0.05
0.1
0.15
0.2
0.25
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
Fishing Grounds FootprintFishing grounds biocapacity
0
0.05
0.1
0.15
0.2
0.25
2001 2003 2005 2007 2009 2011Glo
bal
hec
tare
s P
er c
apit
a
Year
Built-up land FootprintBuilt-up land biocapacity
0.00
0.50
1.00
1.50
2.00
2.50
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er
cap
ita
Year
Co2 FootprintBiocapacity
174
Figure 15B
Trend in Ecological Overshoot of High and Middle Income Countries 2003-11
Source: Author Computation based on GFN data set, 2003-11
Figure 16B
Trend in Ecological Efficiency of High and Middle Income Countries,2003-11
Source: Author Computation based on GFN and World Bank data set, 2003-11
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
2001 2003 2005 2007 2009 2011
in P
erce
nta
ge
Year
HIC Ecological Overshoot
MIC Ecological Overshoot
0
0.5
1
1.5
2
2.5
3
2001 2003 2005 2007 2009 2011
10
00
of
inco
me/
gha
Year
HIC Ecological Efficiency
MIC Ecological Efficiency
175
Figure 17B
Trend between High and Middle Income countries’ Biocapacity, 2003-11
Figure 18B
Trend between High and Middle Income countries’ ecological footprint, 2003-11
Figure 19B
Trend between High and Middle Income countries’ cropland footprint, 2003-11
Figure 20B
Trend between High and Middle Income countries’ grazing footprint, 2003-11
0
0.5
1
1.5
2
2.5
3
3.5
4
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Biocapacity
0
1
2
3
4
5
6
7
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Ecological Footprint
MIC Ecological Footprint
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Cropland FootprintMIC Cropland Footprint
176
Figure 21B
Trend between High and Middle Income countries’ forest footprint, 2003-11
Figure 22B
Trend between High and Middle Income countries’ fish footprint, 2003-11
Figure 23B
Trend between High and Middle Income countries’ built-up footprint, 2003-11
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Grazing land Footprint
MIC Grazing land Footprint
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Forest FootprintMIC Forest Footprint
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Fish FootprintMIC Fish Footprint
177
Figure 24B
Trend between High and Middle Income countries’ CO2 footprint,
2003-11
0
0.05
0.1
0.15
0.2
0.25
0.3
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Built-up land Footprint
MIC Built-up land Footprint
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
2001 2003 2005 2007 2009 2011
Glo
bal
hec
tare
s P
er c
apit
a
Year
HIC Co2 FootprintMIC Co2 Footprint
178
APPENDIX C
Comparison between High and Middle Income Countries’ Atkinson indices
Figure 1C
Comparison between High and Middle Income Countries’ Atkinson indices:
Ecological footprint, per capita income and Environment Intensity
Figure 2C
Comparison between High and Middle Income Countries Atkinson indices:
Cropland footprint, its environment intensity and per capita income
0
0.2
0.4
0.6
0.8
Total Ecological
footprint
Per Capita Income Environmental
Intensity
Atk
inso
n I
nd
ex o
f E
qual
ity
High Income CountriesMiddle Income Countres
0
0.2
0.4
0.6
0.8
Cropland footprint Per Capita Income Environmental
Intensity of
Cropland footprint
Atk
inso
nIn
dex
of
Eq
ual
ity
High Income CountriesMiddle Income Countres
179
Figure 3C
Comparison between High and Middle Income Countries Atkinson indices:
Grazing land footprint, its environment intensity and per capita income
Figure 4C
Comparison between High and Middle Income Countries Atkinson indices:
Forest footprint, its environment intensity and per capita income
Figure 5C
Comparison between High and Middle Income Countries Atkinson indices:
Fish land footprint, its environment intensity and per capita income
00.10.20.30.40.50.60.70.80.9
Grazing land
footprint
Per Capita Income Environmental
Intensity of Grazing
land footprint
Atk
inso
n I
nd
ex o
f E
qual
ity
High Income CountriesMiddle Income Countres
00.10.20.30.40.50.60.70.8
Forest footprint Per Capita Income Environmental
Intensity of Forest
footprintAtk
inso
n I
nd
ex o
f E
qual
ity
High Income CountriesMiddle Income Countres
0
0.2
0.4
0.6
0.8
Fish footprint Per Capita Income Environmental
Intensity of Fish
footprint
Atk
inso
n I
nd
ex o
f E
qual
ity
High Income Countries
Middle Income Countres
180
Figure 6C
Comparison between High and Middle Income Countries Atkinson indices:
Built-up land footprint, its environment intensity and per capita income
Figure 7C
Comparison between High and Middle Income Countries Atkinson indices:
CO2 footprint, its environment intensity and per capita income
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Built-up footprint Per Capita Income Environmental
Intensity of Built-
up footprint
Atk
inso
n I
nd
ex o
f E
qual
ity
High Income CountriesMiddle Income Countres
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Co2 footprint Per Capita Income Environmental
Intensity of Co2
footprint
Atk
inso
n I
nd
ex o
f E
qual
ity
High Income Countries
Middle Income Countres
181
APPENDIX D
Table 1D
Trend in Environmental Impact Intensity of High Income Countries
Years
Country 2003 2005 2007 2009 2011
Trend
Australia 0.323 0.230 0.167 0.126 0.134
Mixed
Austria 0.147 0.130 0.114 0.109 0.100
Declined
Belgium 0.218 0.139 0.180 0.163 0.121
Mixed
Canada 0.315 0.196 0.158 0.145 0.126
Declined
Cyprus 0.14 0.110 0.12 0.139 0.113
Mixed
Czech Republic 0.495 0.402 0.313 0.239 0.208
Declined
Denmark 0.163 0.165 0.141 0.131 0.070
Declined
Estonia 0.688 0.619 0.475 0.340 0.313
Declined
France 0.177 0.141 0.121 0.118 0.095
Declined
Germany 0.155 0.122 0.122 0.108 0.095
Declined
Greece 0.278 0.262 0.189 0.159 0.151
Declined
Hungary 0.367 0.318 0.216 0.232 0.200
Mixed
Ireland 0.129 0.123 0.103 0.110 0.088
Declined
Israel
0.234 0.235 0.193 0.148 0.141
Declined
Italy 0.140 0.149 0.132 0.119 0.108
Mixed
Japan 0.142 0.137 0.139 0.097 0.082
Mixed
Korea 0.232 0.084 0.211 0.234 0.185
Mixed
Kuwait 0.339 0.249 0.140 0.269 0.186
Mixed
182
(Continued...
Years
Country 2003 2005 2007 2009 2011 Trend
Netherlands 0.136 0.106 0.121 0.114 0.083 Mixed
New Zealand 0.395 0.277 0.151 0.096 0.135 Mixed
Norway 0.158 0.104 0.065 0.034 0.047 Mixed
Poland 0.651 0.496 0.387 0.358 0.276 Declined
Portugal 0.283 0.236 0.196 0.191 0.144
Declined
Qatar 0.10 0.11 0.155 0.159 0.078 Mixed
Saudi Arabia 0.443 0.198 0.322 0.249 0.172 Mixed
Singapore 0.145 0.139 0.136 0.163 0.112 Mixed
Slovakia 0.395 0.283 0.253 0.213 0.208 Declined
Slovenia 0.241 0.245 0.222 0.166 0.181 Mixed
Spain 0.217 0.217 0.166 0.133 0.107 Declined
Sweden 0.182 0.118 0.110 0.117 0.109 Mixed
Switzerland 0.086 0.091 0.079 0.072 0.055 Mixed
Trinidad 0.375 0.173 0.187 0.551 0.326 Mixed
UAE 0.295 0.235 0.249 0.277 0.247 Mixed
UK 0.164 0.133 0.101 0.121 0.101 Mixed
USA 0.244 0.213 0.166 0.149 0.136
Declined
183
Table 2D
Trend in Environmental Impact Intensity of Middle Income Countries
Years
Country 2003 2005 2007 2009 2011 Trend
Albania 0.507 0.823 0.530 0.413 0.453
Mixed
Algeria 0.740 0.537 0.403 0.464 0.299
Mixed
Angola 0.818 0.440 0.295 0.280 0.178
Declined
Argentina 0.910 0.435 0.315 0.227 0.208
Declined
Armenia 0.950 0.886 0.568 0.514 0.541
Mixed
Azerbaijan 1.954 1.369 0.486 0.343 0.259
Declined
Belarus 1.800 1.233 0.803 0.792 0.579
Declined
Bolivia 0.948 1.684 1.466 1.455 1.090
Mixed
Bosnia 1.148 2.794 1.978 1.463 1.115
Mixed
Botswana 0.677 1.267 0.665 0.622 0.655
Mixed
Brazil 0.783 0.498 0.401 0.331 0.219
Declined
Bulgaria 0.876 0.705 0.687 0.417 4.150
Mixed
Cameroon 1.401 1.386 0.975 1.030 0.882
Mixed
Chile 0.628 0.388 0.308 0.225 0.267
Mixed
China 1.204 1.210 0.828 0.579 0.447
Mixed
Colombia 0.597 0.278 0.400 0.369 0.231
Mixed
Congo 5.427 2.546 2.754 2.797 2.170
Mixed
Costa Rica 0.460 0.483 0.446 0.351 0.248
Mixed
Côte d'Ivoire 1.020 0.948 0.937 0.730 0.828
Mixed
184
(Continued…
Years
Country 2003 2005 2007 2009 2011 Trend
Cuba 0.466 0.465 0.357 0.346 0.258
Declined
Dominican 0.643 0.404 0.318 0.263 0.218
Declined
Ecuador 0.632 0.728 0.525 0.305 0.331
Mixed
Egypt 1.301 1.392 0.987 0.681 0.618
Mixed
El Salvador 0.468 0.563 0.605 0.583 0.442
Mixed
Gabon 0.431 0.189 0.164 0.305 0.213
Mixed
Georgia 0.983 0.732 0.785 0.746 0.388
Mixed
Ghana 2.853 2.961 1.594 1.552 1.058
Declined
Guatemala 0.813 0.729 0.717 0.688 0.534
Declined
Honduras 1.091 1.261 1.110 0.860 0.654
Mixed
India 1.373 1.226 0.870 0.800 0.618
Declined
Indonesia 1.061 0.751 0.652 0.575 0.365
Declined
Iran 0.882 0.854 0.571 0.515 0.267
Declined
Iraq 0.175 0.169 0.171 0.190 0.198
Mixed
Jamaica 0.579 0.257 0.400 0.401 0.315
Mixed
Jordan 0.784 0.733 0.679 0.522 0.313
Declined
Kazakhstan 1.733 0.894 0.671 0.600 0.490
Declined
Latvia 0.668 0.462 0.402 0.311 0.405
Mixed
Lebanon 0.481 0.577 0.483 0.417 0.332
Mixed
185
(Continued...
Years
Country 2003 2005 2007 2009 2011 Trend
Lesotho 1.689 1.515 1.315 1.279 0.902
Declined
Libya 0.699 0.525 0.272 0.433 0.395
Mixed
Lithuania 0.558 0.407 0.380 0.346 0.305
Declined
Macedonia 1.339 1.504 1.393 0.766 0.533
Mixed
Malaysia 0.712 0.435 0.672 0.410 0.282
Mixed
Mauritania 2.522 2.745 2.587 2.295 1.481
Mixed
Mauritius 0.325 0.441 0.678 0.678 0.333
Mixed
Mexico 0.378 0.428 0.325 0.445 0.246
Mixed
Moldova 2.521 1.483 1.129 0.918 0.858
Declined
Mongolia 3.988 3.499 3.387 3.261 1.201
Declined
Montenegro 0.936 0.711 0.561 0.499 0.456
Declined
Morocco 0.670 0.585 0.486 0.520 0.492
Mixed
Namibia 0.590 1.035 0.514 0.339 0.406
Mixed
Nicaragua 1.510 1.744 1.154 1.014 0.804
Mixed
Nigeria 2.601 1.669 1.270 1.191 2.018
Mixed
Pakistan 1.124 1.154 0.804 0.792 0.552
Mixed
Panama 0.425 0.685 0.468 0.302 0.289
Mixed
Paraguay 2.135 2.135 1.381 1.231 1.044
Declined
Peru 0.526 0.577 0.426 0.383 0.361
Mixed
186
(Continued...
Years
Country 2003 2005 2007 2009 2011 Trend
Philippines 1.158 0.727 0.772 0.653 0.422
Mixed
Romania 0.907 0.614 0.330 0.292 0.272
Declined
Russia 1.508 0.704 0.484 0.467 0.335
Declined
Serbia 0.847 3.380 2.518 2.258 2.331
Mixed
South Africa 1.419 0.590 0.425 0.447 0.383
Mixed
Sri Lanka 0.264 0.188 0.197 0.203 0.145
Mixed
Swaziland 0.747 0.592 0.928 0.977 0.642
Mixed
Syrian 0.948 0.691 0.566 0.634 0.359
Mixed
Thailand 1.209 1.339 1.140 1.010 0.923
Mixed
Timor-Leste 0.149 0.122 0.110 0.083 0.063
Declined
Tunisia 0.606 0.548 0.498 0.408 0.407
Declined
Turkey 0.431 0.381 0.290 0.280 0.251
Declined
Turkmenistan 2.474 2.262 1.506 0.961 0.730
Declined
Ukraine 3.217 1.473 0.946 0.904 0.787
Declined
Uruguay 1.047 1.045 0.732 0.414 0.289
Declined
Uzbekistan 4.824 3.315 2.099 1.608 1.217
Mixed
Venezuela
0.722
0.517
0.348
0.251
0.243
Declined
Vietnam 1.427 1.803 1.523 1.217 0.881
Mixed
Yemen 1.162 1.117 0.797 0.726 0.616
Declined
Zambia 2.930 1.113 0.827 0.793 0.508
Declined
187
Table 3D
Ranking of High Income Countries according to their Ecological efficiency performance:
2003-2011
Country
Ecological efficiency
relative to best performer
(RI/RI of best performer)
Ecological efficiency
relative to mean
(RI/mean of RI)
Ecological
Efficiency Rank
Switzerland 1.00 0.40 1
Norway 1.06 0.43 2
Ireland 1.44 0.58 3
Netherlands 1.45 0.59 4
Cyprus 1.51 0.61 5
Japan 1.55 0.62 6
Austria 1.56 0.63 7
Germany 1.56 0.63 8
UK 1.61 0.65 9
Sweden 1.65 0.67 10
Italy 1.68 0.68 11
France 1.69 0.68 12
Denmark 1.74 0.70 13
Singapore 1.79 0.72 14
Qatar 1.83 0.74 15
Belgium 2.13 0.86 16
Spain 2.18 0.88 17
USA 2.36 0.95 18
188
(Continued...
Country
Ecological efficiency
relative to best
performer (RI/RI of
best performer)
Ecological efficiency
relative to mean
(RI/mean of RI)
Ecological Efficiency
Rank
Canada 2.44 0.99 19
Korea 2.46 0.99 20
Israel 2.47 0.99 21
Australia 2.55 1.03 22
Greece 2.70 1.09 23
Portugal 2.73 1.10 24
New Zealand 2.74 1.10 25
Slovenia 2.74 1.11 26
Kuwait 3.07 1.24 27
Trinidad 3.12 1.26 28
UAE 3.38 1.36 29
Hungary 3.46 1.40 30
Slovakia 3.51 1.42 31
Saudi Arabia 3.59 1.45 32
Czech Republic 4.30 1.73 33
Poland 5.63 2.27 34
Estonia 6.32 2.55 35
189
Table 4D
Ranking of Middle Income Countries according to their Ecological efficiency performance:
2003-2011
Country Ecological efficiency
relative to best
performer (RI/RI of
best performer)
Ecological
efficiency relative
to mean
(RI/mean of RI)
Ecological Efficiency
Rank
Timor-Leste 1.00 0.12 1
Iraq 1.71 0.20 2
Sri Lanka 1.88 0.22 3
Gabon 2.46 0.29 4
Turkey 3.10 0.37 5
Chile 3.43 0.41 6
Mexico 3.44 0.41 7
Dominican 3.48 0.41 8
Colombia 3.54 0.42 9
Cuba 3.57 0.42 10
Jamaica 3.68 0.44 11
Costa Rica 3.75 0.45 12
Lithuania 3.77 0.45 13
Angola 3.80 0.45 14
Venezuela 3.93 0.47 15
Argentina 3.95 0.47 16
Panama 4.09 0.49 17
Brazil 4.21 0.50 18
Latvia 4.24 0.51 19
Peru 4.29 0.51 20
Lebanon 4.32 0.51 21
Libya 4.39 0.52 22
Romania 4.56 0.54 23
Algeria 4.61 0.55 24
Mauritius 4.63 0.55 25
Tunisia 4.66 0.55 26
Malaysia 4.74 0.56 27
190
(Continued...
Country Ecological efficiency
relative to best
performer (RI/RI of best
performer)
Ecological
efficiency relative
to mean (RI/mean
of RI)
Ecological Efficiency
Rank
Ecuador 4.76 0.57 28
El Salvador 5.02 0.60 29
Albania 5.14 0.61 30
Morocco 5.19 0.62 31
Namibia 5.44 0.65 32
Jordan 5.72 0.68 33
Iran 5.83 0.69 34
Montenegro 5.97 0.71 35
Syrian 6.03 0.72 36
South Africa 6.16 0.73 37
Indonesia 6.42 0.76 38
Armenia 6.53 0.78 39
Guatemala 6.57 0.78 40
Russia 6.60 0.79 41
Uruguay 6.66 0.79 42
Georgia 6.86 0.82 43
Philippines 7.04 0.84 44
Swaziland 7.33 0.87 45
Botswana 7.33 0.87 46
China 8.05 0.96 47
Kazakhstan 8.28 0.99 48
Azerbaijan 8.32 0.99 49
Yemen 8.34 0.99 50
191
(Continued...
Country Ecological efficiency relative
to best performer (RI/RI of
best performer)
Ecological efficiency
relative to mean
(RI/mean of RI)
Ecological
Efficiency Rank
Pakistan 8.35 0.99 51
Côte d'Ivoire 8.42 1.00 52
India 9.22 1.10 53
Honduras 9.39 1.12 54
Egypt 9.39 1.12 55
Belarus 9.82 1.17 56
Macedonia 10.45 1.24 57
Thailand 10.60 1.26 58
Cameroon 10.71 1.28 59
Zambia 11.64 1.39 60
Nicaragua 11.75 1.40 61
Bolivia 12.54 1.49 62
Lesotho 12.64 1.51 63
Bulgaria 12.89 1.54 64
Vietnam 12.93 1.54 65
Moldova 13.03 1.55 66
Ukraine 13.82 1.65 67
Paraguay 14.95 1.78 68
Turkmenistan 14.97 1.78 69
Bosnia 16.03 1.91 70
Nigeria 16.51 1.97 71
Ghana 18.90 2.25 72
Serbia 21.39 2.55 73
Mauritania 21.94 2.61 74
Uzbekistan 24.65 2.94 75
Mongolia 28.94 3.45 76
Congo 29.61 3.53 77
192
APPENDIX E
List of High and Middle Income Countries
Table 1E
List of High Income Countries
Australia France Netherlands Spain
Austria Germany New Zealand Sweden
Bahrain Greece Norway Switzerland
Belgium Hungary Poland Trinidad
Canada Ireland Portugal UAE
Cyprus Israel Qatar UK
Czech Republic Italy Saudi Arabia USA
Denmark Japan Singapore
Estonia Korea Slovakia
Finland Kuwait Slovenia
Table 2E
List of Middle income Countries
Albania Dominican Lithuania Serbia
Algeria Ecuador Macedonia South Africa
Angola Egypt Malaysia Sri Lanka
Argentina El Salvador Mauritania Swaziland
Armenia Gabon Mauritius Syrian
Azerbaijan Georgia Mexico Thailand
Belarus Ghana Moldova Timor-Leste
Bolivia Guatemala Mongolia Tunisia
Bosnia Honduras Montenegro Turkey
Botswana India Morocco Turkmenistan
Brazil Indonesia Namibia Ukraine
Bulgaria Iran Nicaragua Uruguay
Cameroon Iraq Nigeria Uzbekistan
Chile Jamaica Pakistan Venezuela
China Jordan Panama Viet Nam
Colombia Kazakhstan Paraguay Yemen
Congo Latvia Peru Zambia
Costa Rica Lebanon Philippines
Côte d'Ivoire Lesotho Romania
Cuba Libyan Russia
193
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